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description Publicationkeyboard_double_arrow_right Article 2020 France, Cyprus, Italy EnglishPublisher:Oxford University Press (OUP) Funded by:EC | IDPfun, EC | chemREPEATEC| IDPfun ,EC| chemREPEATMier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.; Mier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.;Abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 67 citations 67 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 Netherlands, Netherlands, Italy, United Kingdom, United Kingdom, Netherlands, Netherlands, France, United Kingdom, Spain EnglishPublisher:HAL CCSD Funded by:EC | PhenoMeNalEC| PhenoMeNalvan Rijswijk, M; van Rijswijk, M; Beirnaert, C; Beirnaert, C; Caron, C; Cascante, M; Dominguez, V; Dunn, WB; Ebbels, TMD; Giacomoni, F; Gonzalez-Beltran, A; Hankemeier, T; Haug, K; Haug, K; Izquierdo-Garcia, JL; Jimenez, RC; Jimenez, RC; Jourdan, F; Kale, N; Klapa, MI; Kohlbacher, O; Koort, K; Kultima, K; Le Corguillé, G; Moreno, P; Moschonas, NK; Moschonas, NK; Neumann, S; O'Donovan, C; Reczko, M; Rocca-Serra, P; Rosato, A; Rosato, A; Rosato, A; Salek, RM; Salek, RM; Sansone, S-A; Satagopam, V; Schober, D; Shimmo, R; Spicer, RA; Spicer, RA; Spjuth, O; Spjuth, O; Spjuth, O; Thévenot, EA; Thévenot, EA; Viant, MR; Weber, RJM; Willighagen, EL; Willighagen, EL; Zanetti, G; Steinbeck, C; Steinbeck, C;Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases. The meeting was funded by PhenoMeNal, European Commission's Horizon2020 programme, grant agreement number 654241 Sí
HAL - UPEC / UPEM; H... arrow_drop_down Europe PubMed CentralArticle . 2017 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC5627583Data sources: PubMed CentralRecolector de Ciencia Abierta, RECOLECTAArticle . 2017Data sources: Recolector de Ciencia Abierta, RECOLECTAFlore (Florence Research Repository)Article . 2017Data sources: Flore (Florence Research Repository)Oxford University Research Archive; F1000ResearchOther literature type . Article . 2017 . 2018 . Peer-reviewedLicense: CC BYSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositorySpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 25 citations 25 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 34visibility views 34 download downloads 65 Powered bymore_vert HAL - UPEC / UPEM; H... arrow_drop_down Europe PubMed CentralArticle . 2017 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC5627583Data sources: PubMed CentralRecolector de Ciencia Abierta, RECOLECTAArticle . 2017Data sources: Recolector de Ciencia Abierta, RECOLECTAFlore (Florence Research Repository)Article . 2017Data sources: Flore (Florence Research Repository)Oxford University Research Archive; F1000ResearchOther literature type . Article . 2017 . 2018 . Peer-reviewedLicense: CC BYSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositorySpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2016 Spain, Netherlands, Germany, Germany, United Kingdom, Luxembourg, Spain, Sweden, United Kingdom, United Kingdom, United Kingdom EnglishPublisher:Springer Science and Business Media LLC Funded by:EC | BLUEPRINT, EC | SYSMEDIBD, EC | P-MEDICINE +34 projectsEC| BLUEPRINT ,EC| SYSMEDIBD ,EC| P-MEDICINE ,EC| ERA-IB-2 ,EC| PSIMEX ,EC| DECIPHER ,EC| ESGI ,EC| EPIGENESYS ,EC| MEDALL ,EC| ERASYSAPP ,EC| PREDEMICS ,EC| CASYM ,EC| EMIF ,EC| ELIXIR ,EC| MIMOMICS ,EC| TRANSLOCATION ,EC| VPH-SHARE ,EC| BRIDGES ,EC| ASTERIX ,EC| SEMCARE ,EC| IDEAL ,EC| MedBioinformatics ,EC| STATEGRA ,WT| Wellcome Trust Sanger Institute - generic account for deposition of all core- funded research papers ,EC| U-BIOPRED ,EC| ECHO ,WT ,EC| CANCER-ID ,EC| PROTEOMEXCHANGE ,EC| COMBIMS ,EC| ERASYNBIO ,EC| KConnect ,EC| READNA ,EC| PREPARE ,EC| MULTIMOD ,EC| RADIANT ,EC| CHAARMCharles Auffray; Rudi Balling; Inês Barroso; László Bencze; Mikael Benson; Jay M. Bergeron; Enrique Bernal-Delgado; Niklas Blomberg; Christoph Bock; Ana Conesa; Susanna Del Signore; Christophe Delogne; Peter Devilee; Alberto Di Meglio; Marinus J.C. Eijkemans; Paul Flicek; Norbert Graf; Vera Grimm; Henk-Jan Guchelaar; Yike Guo; Ivo Gut; Allan Hanbury; Shahid Hanif; Ralf-Dieter Hilgers; Angel Honrado; D. Rod Hose; Jeanine J. Houwing-Duistermaat; Tim Hubbard; Sophie Helen Janacek; Haralampos Karanikas; Tim Kievits; Manfred Kohler; Andreas Kremer; Jerry Lanfear; Thomas Lengauer; Edith Maes; Theo F. Meert; Werner Müller; Dörthe Nickel; Peter Oledzki; Bertrand Pedersen; Milan Petkovic; Konstantinos Pliakos; Magnus Rattray; Josep Redón Más; Reinhard Schneider; Thierry Sengstag; Xavier Serra-Picamal; Wouter Spek; Lea A. I. Vaas; Okker van Batenburg; Marc Vandelaer; Péter Várnai; Pablo Villoslada; Juan Antonio Vizcaíno; John Peter Mary Wubbe; Gianluigi Zanetti;doi: 10.1186/s13073-016-0323-y , 10.1186/s13073-016-0376-y , 10.18154/rwth-conv-213156 , 10.17863/cam.42076 , 10.18154/rwth-conv-213157
handle: 11858/00-001M-0000-002C-1D02-C , 11858/00-001M-0000-002C-1D04-8 , 1887/99384 , 11858/00-001M-0000-002A-F90A-4 , 11858/00-001M-0000-002A-F908-8 , 1874/337769
pmc: PMC5100330 , PMC4919856
doi: 10.1186/s13073-016-0323-y , 10.1186/s13073-016-0376-y , 10.18154/rwth-conv-213156 , 10.17863/cam.42076 , 10.18154/rwth-conv-213157
handle: 11858/00-001M-0000-002C-1D02-C , 11858/00-001M-0000-002C-1D04-8 , 1887/99384 , 11858/00-001M-0000-002A-F90A-4 , 11858/00-001M-0000-002A-F908-8 , 1874/337769
pmc: PMC5100330 , PMC4919856
Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health arid healthcare for all Europearis. Funding Agencies|European Union [115568, 603160, 282510, 664691, 115749, 305033, 305397, 288028, 242189, 211601]; European Molecular Biology Laboratory; Wellcome Trust [WT098051]; [115372]; [257082]; [291814]; [291728]; [321567]; [262055]; [115446]; [602552]; [644753]; [634143]; [261357]; [305280]; [115525]; [2011 23 02]; [270089]; [278433]; [602525]; [201418]; [242135]; [260558]; [223411]; [305626]; [115621]; [611388]; [306000]; [354457]; [305564]; [115010]; [269978]
Genome Medicine arrow_drop_down Genome MedicineOther literature type . Article . 2016 . Peer-reviewedEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC4919856Data sources: PubMed CentralEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5100330Data sources: PubMed CentralPublikationsserver der RWTH Aachen UniversityArticle . 2016Data sources: Publikationsserver der RWTH Aachen UniversitySpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTARecolector de Ciencia Abierta, RECOLECTA; Digital Repository of University of ZaragozaArticle . 2016License: CC BYThe University of Manchester - Institutional RepositoryArticle . 2016Data sources: The University of Manchester - Institutional RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTAOpen Repository and Bibliography - LuxembourgArticle . 2016Data sources: Open Repository and Bibliography - LuxembourgLUMC Scholarly Publications; NARCISOther literature type . Article . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 206 citations 206 popularity Top 1% influence Top 1% impulse Top 1% Powered by BIP!visibility 31visibility views 31 download downloads 150 Powered bymore_vert Genome Medicine arrow_drop_down Genome MedicineOther literature type . Article . 2016 . Peer-reviewedEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC4919856Data sources: PubMed CentralEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5100330Data sources: PubMed CentralPublikationsserver der RWTH Aachen UniversityArticle . 2016Data sources: Publikationsserver der RWTH Aachen UniversitySpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTARecolector de Ciencia Abierta, RECOLECTA; Digital Repository of University of ZaragozaArticle . 2016License: CC BYThe University of Manchester - Institutional RepositoryArticle . 2016Data sources: The University of Manchester - Institutional RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTAOpen Repository and Bibliography - LuxembourgArticle . 2016Data sources: Open Repository and Bibliography - LuxembourgLUMC Scholarly Publications; NARCISOther literature type . Article . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1186/s13073-016-0323-y&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 United Kingdom, United States EnglishPublisher:BioMed Central Funded by:EC | ePerMed, WT | Statistical methods for t...EC| ePerMed ,WT| Statistical methods for the analysis of genome-wide association and re-sequencing studies.Mägi, R; Suleimanov, Y; Clarke, G; Kaakinen, M; Fischer, K; Prokopenko, I; Morris, A;pmc: PMC5225593
pmid: 28077070
Background Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. Results We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements “reverse regression” methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10[superscript −8]), which has not been reported in previous large-scale GWAS of lipid traits. Conclusions The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach. Royal Society (Great Britain) (Newton International Alumni Scheme)
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5225593Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2017License: CC BYData sources: Oxford University Research ArchiveSpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu11 citations 11 popularity Average influence Average impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5225593Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2017License: CC BYData sources: Oxford University Research ArchiveSpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5225593&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article 2019 United Kingdom EnglishPublisher:Oxford University Press (OUP) Authors: Watson, O; Cortes-Ciriano, I; Taylor, A; Watson, J;Watson, O; Cortes-Ciriano, I; Taylor, A; Watson, J;pmc: PMC6853675
pmid: 31070704
Abstract Motivation Artificial intelligence, trained via machine learning (e.g. neural nets, random forests) or computational statistical algorithms (e.g. support vector machines, ridge regression), holds much promise for the improvement of small-molecule drug discovery. However, small-molecule structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs. Results The quantile-activity bootstrap is proposed as a new model validation framework using quantile splits on the activity distribution function to construct training and testing sets. In addition, we propose two novel rank-based loss functions which penalize only the out-of-sample predicted ranks of high-activity molecules. The combination of these methods was used to assess the performance of neural nets, random forests, support vector machines (regression) and ridge regression applied to 25 diverse high-quality structure-activity datasets publicly available on ChEMBL. Model validation based on random partitioning of available data favours models that overfit and ‘memorize’ the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes extrapolation of models onto structurally different molecules outside of the training data. Simpler, traditional statistical methods such as ridge regression can outperform state-of-the-art machine learning methods in this setting. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand. Availability and implementation All software and data are available as Jupyter notebooks found at https://github.com/owatson/QuantileBootstrap. Supplementary information Supplementary data are available at Bioinformatics online.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6853675Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2019License: CC BYData sources: Oxford University Research Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC6853675&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!visibility 1visibility views 1 download downloads 53 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6853675Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2019License: CC BYData sources: Oxford University Research Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC6853675&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016 United Kingdom, Italy EnglishPublisher:Oxford University Press (OUP) Funded by:UKRI | Implications of the Vagin..., EC | PhenoMeNalUKRI| Implications of the Vaginal Microbiome in Preterm Premature Rupture of Membranes (PPROM) ,EC| PhenoMeNalCacciatore, Stefano; Tenori, Leonardo; Luchinat, Claudio; Bennett, Phillip R; MacIntyre, David A;Summary: KODAMA, a novel learning algorithm for unsupervised feature extraction, is specifically designed for analysing noisy and high-dimensional datasets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The package requires no additional software and runs on all major platforms. Availability and Implementation: KODAMA is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later). Contact: s.cacciatore@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository); BioinformaticsArticle . 2016License: CC BYEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5408808Data sources: PubMed CentralFlore (Florence Research Repository)Article . 2016Data sources: Flore (Florence Research Repository)Spiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2158/1080735&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository); BioinformaticsArticle . 2016License: CC BYEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5408808Data sources: PubMed CentralFlore (Florence Research Repository)Article . 2016Data sources: Flore (Florence Research Repository)Spiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016 United Kingdom EnglishPublisher:BioMed Central Funded by:EC | REGVARMHC, WT | Understanding the genetic...EC| REGVARMHC ,WT| Understanding the genetic basis of common human diseases: core funding for the Wellcome Trust Centre for Human Genetics.Authors: Hai Fang; Bogdan Knezevic; Katie L. Burnham; Julian C. Knight;Hai Fang; Bogdan Knezevic; Katie L. Burnham; Julian C. Knight;pmc: PMC5154134
pmid: 27964755
Background Biological interpretation of genomic summary data such as those resulting from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks in medical genomics research, calling for efficient and integrative tools to resolve this problem. Results We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretation of genomic summary data enabling downstream knowledge discovery. Targeting users of varying computational skills, XGR utilises prior biological knowledge and relationships in a highly integrated but easily accessible way to make user-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, and systems biology network-driven approaches, XGR generates more informative results than conventional analyses. We apply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immune response and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting the concept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can define SNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achieves functional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploring annotation-based relationships between genetic variants. Conclusions XGR provides a single integrated solution to enhance interpretation of genomic summary data for downstream biological discovery. XGR is released as both an R package and a web-app, freely available at http://galahad.well.ox.ac.uk/XGR. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0384-y) contains supplementary material, which is available to authorized users.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5154134Data sources: PubMed CentralOxford University Research Archive; Genome MedicineOther literature type . Article . 2016 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu71 citations 71 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5154134Data sources: PubMed CentralOxford University Research Archive; Genome MedicineOther literature type . Article . 2016 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 Cyprus, United States EnglishPublisher:Public Library of Science (PLoS) Authors: Chasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; +28 AuthorsChasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; Iliopoulos, Ioannis; Kappas, Ilias; Karapiperis, Christos; Kyrpides, Nikos C.; Pafilis, Evangelos; Panteris, Eleftherios; Topalis, Pantelis; Tsiamis, George; Vizirianakis, Ioannis S.; Vlassi, Metaxia; Promponas, Vasilis J.; Ouzounis, Christos A.; Chasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; Iliopoulos, Ioannis; Kappas, Ilias; Karapiperis, Christos; Kyrpides, Nikos C.; Pafilis, Evangelos; Panteris, Eleftherios; Topalis, Pantelis; Tsiamis, George; Vizirianakis, Ioannis S.; Vlassi, Metaxia; Promponas, Vasilis J.; Ouzounis, Christos A.;Author(s): Chasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; Iliopoulos, Ioannis; Kappas, Ilias; Karapiperis, Christos; Kyrpides, Nikos C; Pafilis, Evangelos; Panteris, Eleftherios; Topalis, Pantelis; Tsiamis, George; Vizirianakis, Ioannis S; Vlassi, Metaxia; Promponas, Vasilis J; Ouzounis, Christos A | Abstract: We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development. We compare output to other countries of similar geography, economy, and size—based on publication counts recorded in the literature—and predict future growth based on those counts as well as national priority areas. Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries. Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6922331Data sources: PubMed CentralOpenAIRE; eScholarship - University of CaliforniaArticle . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007532&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6922331Data sources: PubMed CentralOpenAIRE; eScholarship - University of CaliforniaArticle . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007532&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020 Luxembourg, Italy, France, United Kingdom, Italy, France, Germany, Spain EnglishPublisher:Public Library of Science (PLoS) Funded by:EC | ELIXIR-EXCELERATEEC| ELIXIR-EXCELERATEGurwitz, Kim T; Singh Gaur, Prakash; Bellis, Louisa J; Larcombe, Lee; Alloza, Eva; Balint, Balint Laszlo; Botzki, Alexander; Dimec, Jure; Dominguez Del Angel, Victoria; Fernandes, Pedro L; Korpelainen, Eija; Krause, Roland; Kuzak, Mateusz; Le Pera, Loredana; Leskošek, Brane; Lindvall, Jessica M; Marek, Diana; Martinez, Paula A; Muyldermans, Tuur; Nygård, Ståle; Palagi, Patricia M; Peterson, Hedi; Psomopoulos, Fotis; Spiwok, Vojtech; Van Gelder, Celia WG; Via, Allegra; Vidak, Marko; Wibberg, Daniel; Morgan, Sarah L; Rustici, Gabriella;ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR’s framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course. ELIXIR-EXCELERATE is funded by the European Commission within the Research Infrastructures programme of Horizon 2020, grant agreement number 676559 (https://ec.europa.eu/programmes/horizon2020/en/area/researchinfrastructures). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC7377377Data sources: PubMed CentralServeur académique lausannoisArticle . 2020License: CC BYData sources: Serveur académique lausannoisPLoS Computational Biology; Publications at Bielefeld University; Recolector de Ciencia Abierta, RECOLECTA; CNR ExploRAOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYRecolector de Ciencia Abierta, RECOLECTAOther literature type . Article . 2020License: CC BYData sources: Recolector de Ciencia Abierta, RECOLECTAUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2020License: CC BYData sources: UPCommons. Portal del coneixement obert de la UPCOpen Repository and Bibliography - LuxembourgArticle . 2020Data sources: Open Repository and Bibliography - Luxembourgadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007976&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 125visibility views 125 download downloads 204 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC7377377Data sources: PubMed CentralServeur académique lausannoisArticle . 2020License: CC BYData sources: Serveur académique lausannoisPLoS Computational Biology; Publications at Bielefeld University; Recolector de Ciencia Abierta, RECOLECTA; CNR ExploRAOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYRecolector de Ciencia Abierta, RECOLECTAOther literature type . Article . 2020License: CC BYData sources: Recolector de Ciencia Abierta, RECOLECTAUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2020License: CC BYData sources: UPCommons. Portal del coneixement obert de la UPCOpen Repository and Bibliography - LuxembourgArticle . 2020Data sources: Open Repository and Bibliography - Luxembourgadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007976&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2017 EnglishPublisher:BioMed Central Funded by:EC | FORECEEEC| FORECEEAuthors: Yuting Chen; Martin Widschwendter; Andrew E. Teschendorff;Yuting Chen; Martin Widschwendter; Andrew E. Teschendorff;pmc: PMC5738803
pmid: 29262847
Background Diverse molecular alterations associated with smoking in normal and precursor lung cancer cells have been reported, yet their role in lung cancer etiology remains unclear. A prominent example is hypomethylation of the aryl hydrocarbon-receptor repressor (AHRR) locus, which is observed in blood and squamous epithelial cells of smokers, but not in lung cancer. Results Using a novel systems-epigenomics algorithm, called SEPIRA, which leverages the power of a large RNA-sequencing expression compendium to infer regulatory activity from messenger RNA expression or DNA methylation (DNAm) profiles, we infer the landscape of binding activity of lung-specific transcription factors (TFs) in lung carcinogenesis. We show that lung-specific TFs become preferentially inactivated in lung cancer and precursor lung cancer lesions and further demonstrate that these results can be derived using only DNAm data. We identify subsets of TFs which become inactivated in precursor cells. Among these regulatory factors, we identify AHR, the aryl hydrocarbon-receptor which controls a healthy immune response in the lung epithelium and whose repressor, AHRR, has recently been implicated in smoking-mediated lung cancer. In addition, we identify FOXJ1, a TF which promotes growth of airway cilia and effective clearance of the lung airway epithelium from carcinogens. Conclusions We identify TFs, such as AHR, which become inactivated in the earliest stages of lung cancer and which, unlike AHRR hypomethylation, are also inactivated in lung cancer itself. The novel systems-epigenomics algorithm SEPIRA will be useful to the wider epigenome-wide association study community as a means of inferring regulatory activity. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1366-0) contains supplementary material, which is available to authorized users.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5738803Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5738803&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 24 citations 24 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5738803Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5738803&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article 2020 France, Cyprus, Italy EnglishPublisher:Oxford University Press (OUP) Funded by:EC | IDPfun, EC | chemREPEATEC| IDPfun ,EC| chemREPEATMier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.; Mier, Pablo; Paladin, Lisanna; Tamana, Stella; Petrosian, Sophia; Hajdu-Soltész, Borbála; Urbanek, Annika; Gruca, Aleksandra; Plewczynski, Dariusz; Grynberg, Marcin; Bernadó, Pau; Gáspári, Zoltán; Ouzounis, Christos A.; Promponas, Vasilis J.; Kajava, Andrey V.; Hancock, John M.; Tosatto, Silvio C. E.; Dosztanyi, Zsuzsanna; Andrade-Navarro, Miguel A.;Abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1093/bib/bbz007&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 67 citations 67 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC7299295Data sources: PubMed CentralArchivio istituzionale della ricerca - Università di Padova; Briefings in BioinformaticsOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BY NCadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1093/bib/bbz007&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 Netherlands, Netherlands, Italy, United Kingdom, United Kingdom, Netherlands, Netherlands, France, United Kingdom, Spain EnglishPublisher:HAL CCSD Funded by:EC | PhenoMeNalEC| PhenoMeNalvan Rijswijk, M; van Rijswijk, M; Beirnaert, C; Beirnaert, C; Caron, C; Cascante, M; Dominguez, V; Dunn, WB; Ebbels, TMD; Giacomoni, F; Gonzalez-Beltran, A; Hankemeier, T; Haug, K; Haug, K; Izquierdo-Garcia, JL; Jimenez, RC; Jimenez, RC; Jourdan, F; Kale, N; Klapa, MI; Kohlbacher, O; Koort, K; Kultima, K; Le Corguillé, G; Moreno, P; Moschonas, NK; Moschonas, NK; Neumann, S; O'Donovan, C; Reczko, M; Rocca-Serra, P; Rosato, A; Rosato, A; Rosato, A; Salek, RM; Salek, RM; Sansone, S-A; Satagopam, V; Schober, D; Shimmo, R; Spicer, RA; Spicer, RA; Spjuth, O; Spjuth, O; Spjuth, O; Thévenot, EA; Thévenot, EA; Viant, MR; Weber, RJM; Willighagen, EL; Willighagen, EL; Zanetti, G; Steinbeck, C; Steinbeck, C;Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases. The meeting was funded by PhenoMeNal, European Commission's Horizon2020 programme, grant agreement number 654241 Sí
HAL - UPEC / UPEM; H... arrow_drop_down Europe PubMed CentralArticle . 2017 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC5627583Data sources: PubMed CentralRecolector de Ciencia Abierta, RECOLECTAArticle . 2017Data sources: Recolector de Ciencia Abierta, RECOLECTAFlore (Florence Research Repository)Article . 2017Data sources: Flore (Florence Research Repository)Oxford University Research Archive; F1000ResearchOther literature type . Article . 2017 . 2018 . Peer-reviewedLicense: CC BYSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositorySpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.12688/f1000research.12342.1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 25 citations 25 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 34visibility views 34 download downloads 65 Powered bymore_vert HAL - UPEC / UPEM; H... arrow_drop_down Europe PubMed CentralArticle . 2017 . Peer-reviewedFull-Text: http://europepmc.org/articles/PMC5627583Data sources: PubMed CentralRecolector de Ciencia Abierta, RECOLECTAArticle . 2017Data sources: Recolector de Ciencia Abierta, RECOLECTAFlore (Florence Research Repository)Article . 2017Data sources: Flore (Florence Research Repository)Oxford University Research Archive; F1000ResearchOther literature type . Article . 2017 . 2018 . Peer-reviewedLicense: CC BYSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositorySpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2016 Spain, Netherlands, Germany, Germany, United Kingdom, Luxembourg, Spain, Sweden, United Kingdom, United Kingdom, United Kingdom EnglishPublisher:Springer Science and Business Media LLC Funded by:EC | BLUEPRINT, EC | SYSMEDIBD, EC | P-MEDICINE +34 projectsEC| BLUEPRINT ,EC| SYSMEDIBD ,EC| P-MEDICINE ,EC| ERA-IB-2 ,EC| PSIMEX ,EC| DECIPHER ,EC| ESGI ,EC| EPIGENESYS ,EC| MEDALL ,EC| ERASYSAPP ,EC| PREDEMICS ,EC| CASYM ,EC| EMIF ,EC| ELIXIR ,EC| MIMOMICS ,EC| TRANSLOCATION ,EC| VPH-SHARE ,EC| BRIDGES ,EC| ASTERIX ,EC| SEMCARE ,EC| IDEAL ,EC| MedBioinformatics ,EC| STATEGRA ,WT| Wellcome Trust Sanger Institute - generic account for deposition of all core- funded research papers ,EC| U-BIOPRED ,EC| ECHO ,WT ,EC| CANCER-ID ,EC| PROTEOMEXCHANGE ,EC| COMBIMS ,EC| ERASYNBIO ,EC| KConnect ,EC| READNA ,EC| PREPARE ,EC| MULTIMOD ,EC| RADIANT ,EC| CHAARMCharles Auffray; Rudi Balling; Inês Barroso; László Bencze; Mikael Benson; Jay M. Bergeron; Enrique Bernal-Delgado; Niklas Blomberg; Christoph Bock; Ana Conesa; Susanna Del Signore; Christophe Delogne; Peter Devilee; Alberto Di Meglio; Marinus J.C. Eijkemans; Paul Flicek; Norbert Graf; Vera Grimm; Henk-Jan Guchelaar; Yike Guo; Ivo Gut; Allan Hanbury; Shahid Hanif; Ralf-Dieter Hilgers; Angel Honrado; D. Rod Hose; Jeanine J. Houwing-Duistermaat; Tim Hubbard; Sophie Helen Janacek; Haralampos Karanikas; Tim Kievits; Manfred Kohler; Andreas Kremer; Jerry Lanfear; Thomas Lengauer; Edith Maes; Theo F. Meert; Werner Müller; Dörthe Nickel; Peter Oledzki; Bertrand Pedersen; Milan Petkovic; Konstantinos Pliakos; Magnus Rattray; Josep Redón Más; Reinhard Schneider; Thierry Sengstag; Xavier Serra-Picamal; Wouter Spek; Lea A. I. Vaas; Okker van Batenburg; Marc Vandelaer; Péter Várnai; Pablo Villoslada; Juan Antonio Vizcaíno; John Peter Mary Wubbe; Gianluigi Zanetti;doi: 10.1186/s13073-016-0323-y , 10.1186/s13073-016-0376-y , 10.18154/rwth-conv-213156 , 10.17863/cam.42076 , 10.18154/rwth-conv-213157
handle: 11858/00-001M-0000-002C-1D02-C , 11858/00-001M-0000-002C-1D04-8 , 1887/99384 , 11858/00-001M-0000-002A-F90A-4 , 11858/00-001M-0000-002A-F908-8 , 1874/337769
pmc: PMC5100330 , PMC4919856
doi: 10.1186/s13073-016-0323-y , 10.1186/s13073-016-0376-y , 10.18154/rwth-conv-213156 , 10.17863/cam.42076 , 10.18154/rwth-conv-213157
handle: 11858/00-001M-0000-002C-1D02-C , 11858/00-001M-0000-002C-1D04-8 , 1887/99384 , 11858/00-001M-0000-002A-F90A-4 , 11858/00-001M-0000-002A-F908-8 , 1874/337769
pmc: PMC5100330 , PMC4919856
Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health arid healthcare for all Europearis. Funding Agencies|European Union [115568, 603160, 282510, 664691, 115749, 305033, 305397, 288028, 242189, 211601]; European Molecular Biology Laboratory; Wellcome Trust [WT098051]; [115372]; [257082]; [291814]; [291728]; [321567]; [262055]; [115446]; [602552]; [644753]; [634143]; [261357]; [305280]; [115525]; [2011 23 02]; [270089]; [278433]; [602525]; [201418]; [242135]; [260558]; [223411]; [305626]; [115621]; [611388]; [306000]; [354457]; [305564]; [115010]; [269978]
Genome Medicine arrow_drop_down Genome MedicineOther literature type . Article . 2016 . Peer-reviewedEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC4919856Data sources: PubMed CentralEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5100330Data sources: PubMed CentralPublikationsserver der RWTH Aachen UniversityArticle . 2016Data sources: Publikationsserver der RWTH Aachen UniversitySpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTARecolector de Ciencia Abierta, RECOLECTA; Digital Repository of University of ZaragozaArticle . 2016License: CC BYThe University of Manchester - Institutional RepositoryArticle . 2016Data sources: The University of Manchester - Institutional RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTAOpen Repository and Bibliography - LuxembourgArticle . 2016Data sources: Open Repository and Bibliography - LuxembourgLUMC Scholarly Publications; NARCISOther literature type . Article . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1186/s13073-016-0323-y&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 206 citations 206 popularity Top 1% influence Top 1% impulse Top 1% Powered by BIP!visibility 31visibility views 31 download downloads 150 Powered bymore_vert Genome Medicine arrow_drop_down Genome MedicineOther literature type . Article . 2016 . Peer-reviewedEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC4919856Data sources: PubMed CentralEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5100330Data sources: PubMed CentralPublikationsserver der RWTH Aachen UniversityArticle . 2016Data sources: Publikationsserver der RWTH Aachen UniversitySpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTARecolector de Ciencia Abierta, RECOLECTA; Digital Repository of University of ZaragozaArticle . 2016License: CC BYThe University of Manchester - Institutional RepositoryArticle . 2016Data sources: The University of Manchester - Institutional RepositoryRecolector de Ciencia Abierta, RECOLECTAArticle . 2016Data sources: Recolector de Ciencia Abierta, RECOLECTAOpen Repository and Bibliography - LuxembourgArticle . 2016Data sources: Open Repository and Bibliography - LuxembourgLUMC Scholarly Publications; NARCISOther literature type . Article . 2016add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1186/s13073-016-0323-y&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 United Kingdom, United States EnglishPublisher:BioMed Central Funded by:EC | ePerMed, WT | Statistical methods for t...EC| ePerMed ,WT| Statistical methods for the analysis of genome-wide association and re-sequencing studies.Mägi, R; Suleimanov, Y; Clarke, G; Kaakinen, M; Fischer, K; Prokopenko, I; Morris, A;pmc: PMC5225593
pmid: 28077070
Background Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. Results We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements “reverse regression” methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10[superscript −8]), which has not been reported in previous large-scale GWAS of lipid traits. Conclusions The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach. Royal Society (Great Britain) (Newton International Alumni Scheme)
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5225593Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2017License: CC BYData sources: Oxford University Research ArchiveSpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5225593&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu11 citations 11 popularity Average influence Average impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5225593Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2017License: CC BYData sources: Oxford University Research ArchiveSpiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5225593&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article 2019 United Kingdom EnglishPublisher:Oxford University Press (OUP) Authors: Watson, O; Cortes-Ciriano, I; Taylor, A; Watson, J;Watson, O; Cortes-Ciriano, I; Taylor, A; Watson, J;pmc: PMC6853675
pmid: 31070704
Abstract Motivation Artificial intelligence, trained via machine learning (e.g. neural nets, random forests) or computational statistical algorithms (e.g. support vector machines, ridge regression), holds much promise for the improvement of small-molecule drug discovery. However, small-molecule structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs. Results The quantile-activity bootstrap is proposed as a new model validation framework using quantile splits on the activity distribution function to construct training and testing sets. In addition, we propose two novel rank-based loss functions which penalize only the out-of-sample predicted ranks of high-activity molecules. The combination of these methods was used to assess the performance of neural nets, random forests, support vector machines (regression) and ridge regression applied to 25 diverse high-quality structure-activity datasets publicly available on ChEMBL. Model validation based on random partitioning of available data favours models that overfit and ‘memorize’ the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes extrapolation of models onto structurally different molecules outside of the training data. Simpler, traditional statistical methods such as ridge regression can outperform state-of-the-art machine learning methods in this setting. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand. Availability and implementation All software and data are available as Jupyter notebooks found at https://github.com/owatson/QuantileBootstrap. Supplementary information Supplementary data are available at Bioinformatics online.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6853675Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2019License: CC BYData sources: Oxford University Research Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC6853675&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!visibility 1visibility views 1 download downloads 53 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6853675Data sources: PubMed CentralOxford University Research ArchiveOther literature type . 2019License: CC BYData sources: Oxford University Research Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC6853675&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016 United Kingdom, Italy EnglishPublisher:Oxford University Press (OUP) Funded by:UKRI | Implications of the Vagin..., EC | PhenoMeNalUKRI| Implications of the Vaginal Microbiome in Preterm Premature Rupture of Membranes (PPROM) ,EC| PhenoMeNalCacciatore, Stefano; Tenori, Leonardo; Luchinat, Claudio; Bennett, Phillip R; MacIntyre, David A;Summary: KODAMA, a novel learning algorithm for unsupervised feature extraction, is specifically designed for analysing noisy and high-dimensional datasets. Here we present an R package of the algorithm with additional functions that allow improved interpretation of high-dimensional data. The package requires no additional software and runs on all major platforms. Availability and Implementation: KODAMA is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later). Contact: s.cacciatore@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository); BioinformaticsArticle . 2016License: CC BYEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5408808Data sources: PubMed CentralFlore (Florence Research Repository)Article . 2016Data sources: Flore (Florence Research Repository)Spiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2158/1080735&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Flore (Florence Rese... arrow_drop_down Flore (Florence Research Repository); BioinformaticsArticle . 2016License: CC BYEurope PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5408808Data sources: PubMed CentralFlore (Florence Research Repository)Article . 2016Data sources: Flore (Florence Research Repository)Spiral - Imperial College Digital RepositoryArticle . 2016Data sources: Spiral - Imperial College Digital Repositoryadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2158/1080735&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016 United Kingdom EnglishPublisher:BioMed Central Funded by:EC | REGVARMHC, WT | Understanding the genetic...EC| REGVARMHC ,WT| Understanding the genetic basis of common human diseases: core funding for the Wellcome Trust Centre for Human Genetics.Authors: Hai Fang; Bogdan Knezevic; Katie L. Burnham; Julian C. Knight;Hai Fang; Bogdan Knezevic; Katie L. Burnham; Julian C. Knight;pmc: PMC5154134
pmid: 27964755
Background Biological interpretation of genomic summary data such as those resulting from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks in medical genomics research, calling for efficient and integrative tools to resolve this problem. Results We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretation of genomic summary data enabling downstream knowledge discovery. Targeting users of varying computational skills, XGR utilises prior biological knowledge and relationships in a highly integrated but easily accessible way to make user-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, and systems biology network-driven approaches, XGR generates more informative results than conventional analyses. We apply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immune response and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting the concept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can define SNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achieves functional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploring annotation-based relationships between genetic variants. Conclusions XGR provides a single integrated solution to enhance interpretation of genomic summary data for downstream biological discovery. XGR is released as both an R package and a web-app, freely available at http://galahad.well.ox.ac.uk/XGR. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0384-y) contains supplementary material, which is available to authorized users.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5154134Data sources: PubMed CentralOxford University Research Archive; Genome MedicineOther literature type . Article . 2016 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5154134&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu71 citations 71 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2016Full-Text: http://europepmc.org/articles/PMC5154134Data sources: PubMed CentralOxford University Research Archive; Genome MedicineOther literature type . Article . 2016 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5154134&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 Cyprus, United States EnglishPublisher:Public Library of Science (PLoS) Authors: Chasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; +28 AuthorsChasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; Iliopoulos, Ioannis; Kappas, Ilias; Karapiperis, Christos; Kyrpides, Nikos C.; Pafilis, Evangelos; Panteris, Eleftherios; Topalis, Pantelis; Tsiamis, George; Vizirianakis, Ioannis S.; Vlassi, Metaxia; Promponas, Vasilis J.; Ouzounis, Christos A.; Chasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; Iliopoulos, Ioannis; Kappas, Ilias; Karapiperis, Christos; Kyrpides, Nikos C.; Pafilis, Evangelos; Panteris, Eleftherios; Topalis, Pantelis; Tsiamis, George; Vizirianakis, Ioannis S.; Vlassi, Metaxia; Promponas, Vasilis J.; Ouzounis, Christos A.;Author(s): Chasapi, Anastasia; Aivaliotis, Michalis; Angelis, Lefteris; Chanalaris, Anastasios; Iliopoulos, Ioannis; Kappas, Ilias; Karapiperis, Christos; Kyrpides, Nikos C; Pafilis, Evangelos; Panteris, Eleftherios; Topalis, Pantelis; Tsiamis, George; Vizirianakis, Ioannis S; Vlassi, Metaxia; Promponas, Vasilis J; Ouzounis, Christos A | Abstract: We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development. We compare output to other countries of similar geography, economy, and size—based on publication counts recorded in the literature—and predict future growth based on those counts as well as national priority areas. Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries. Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6922331Data sources: PubMed CentralOpenAIRE; eScholarship - University of CaliforniaArticle . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007532&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2019Full-Text: http://europepmc.org/articles/PMC6922331Data sources: PubMed CentralOpenAIRE; eScholarship - University of CaliforniaArticle . 2019add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007532&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020 Luxembourg, Italy, France, United Kingdom, Italy, France, Germany, Spain EnglishPublisher:Public Library of Science (PLoS) Funded by:EC | ELIXIR-EXCELERATEEC| ELIXIR-EXCELERATEGurwitz, Kim T; Singh Gaur, Prakash; Bellis, Louisa J; Larcombe, Lee; Alloza, Eva; Balint, Balint Laszlo; Botzki, Alexander; Dimec, Jure; Dominguez Del Angel, Victoria; Fernandes, Pedro L; Korpelainen, Eija; Krause, Roland; Kuzak, Mateusz; Le Pera, Loredana; Leskošek, Brane; Lindvall, Jessica M; Marek, Diana; Martinez, Paula A; Muyldermans, Tuur; Nygård, Ståle; Palagi, Patricia M; Peterson, Hedi; Psomopoulos, Fotis; Spiwok, Vojtech; Van Gelder, Celia WG; Via, Allegra; Vidak, Marko; Wibberg, Daniel; Morgan, Sarah L; Rustici, Gabriella;ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR’s framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course. ELIXIR-EXCELERATE is funded by the European Commission within the Research Infrastructures programme of Horizon 2020, grant agreement number 676559 (https://ec.europa.eu/programmes/horizon2020/en/area/researchinfrastructures). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC7377377Data sources: PubMed CentralServeur académique lausannoisArticle . 2020License: CC BYData sources: Serveur académique lausannoisPLoS Computational Biology; Publications at Bielefeld University; Recolector de Ciencia Abierta, RECOLECTA; CNR ExploRAOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYRecolector de Ciencia Abierta, RECOLECTAOther literature type . Article . 2020License: CC BYData sources: Recolector de Ciencia Abierta, RECOLECTAUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2020License: CC BYData sources: UPCommons. Portal del coneixement obert de la UPCOpen Repository and Bibliography - LuxembourgArticle . 2020Data sources: Open Repository and Bibliography - Luxembourgadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007976&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 125visibility views 125 download downloads 204 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC7377377Data sources: PubMed CentralServeur académique lausannoisArticle . 2020License: CC BYData sources: Serveur académique lausannoisPLoS Computational Biology; Publications at Bielefeld University; Recolector de Ciencia Abierta, RECOLECTA; CNR ExploRAOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYRecolector de Ciencia Abierta, RECOLECTAOther literature type . Article . 2020License: CC BYData sources: Recolector de Ciencia Abierta, RECOLECTAUPCommons. Portal del coneixement obert de la UPCOther literature type . Article . 2020License: CC BYData sources: UPCommons. Portal del coneixement obert de la UPCOpen Repository and Bibliography - LuxembourgArticle . 2020Data sources: Open Repository and Bibliography - Luxembourgadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1371/journal.pcbi.1007976&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2017 EnglishPublisher:BioMed Central Funded by:EC | FORECEEEC| FORECEEAuthors: Yuting Chen; Martin Widschwendter; Andrew E. Teschendorff;Yuting Chen; Martin Widschwendter; Andrew E. Teschendorff;pmc: PMC5738803
pmid: 29262847
Background Diverse molecular alterations associated with smoking in normal and precursor lung cancer cells have been reported, yet their role in lung cancer etiology remains unclear. A prominent example is hypomethylation of the aryl hydrocarbon-receptor repressor (AHRR) locus, which is observed in blood and squamous epithelial cells of smokers, but not in lung cancer. Results Using a novel systems-epigenomics algorithm, called SEPIRA, which leverages the power of a large RNA-sequencing expression compendium to infer regulatory activity from messenger RNA expression or DNA methylation (DNAm) profiles, we infer the landscape of binding activity of lung-specific transcription factors (TFs) in lung carcinogenesis. We show that lung-specific TFs become preferentially inactivated in lung cancer and precursor lung cancer lesions and further demonstrate that these results can be derived using only DNAm data. We identify subsets of TFs which become inactivated in precursor cells. Among these regulatory factors, we identify AHR, the aryl hydrocarbon-receptor which controls a healthy immune response in the lung epithelium and whose repressor, AHRR, has recently been implicated in smoking-mediated lung cancer. In addition, we identify FOXJ1, a TF which promotes growth of airway cilia and effective clearance of the lung airway epithelium from carcinogens. Conclusions We identify TFs, such as AHR, which become inactivated in the earliest stages of lung cancer and which, unlike AHRR hypomethylation, are also inactivated in lung cancer itself. The novel systems-epigenomics algorithm SEPIRA will be useful to the wider epigenome-wide association study community as a means of inferring regulatory activity. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1366-0) contains supplementary material, which is available to authorized users.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5738803Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5738803&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 24 citations 24 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2017Full-Text: http://europepmc.org/articles/PMC5738803Data sources: PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=PMC5738803&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu