@incollection{Callahan2012, author = {Callahan, Alison and Dumontier, Michel}, doi = {10.1007/978-3-642-30284-8_50}, journal = {Lecture Notes in Computer Science}, month = {jan}, pages = {647-658}, title = {Evaluating Scientific Hypotheses Using the SPARQL Inferencing Notation}, url = {https://link.springer.com/content/pdf/10.1007%2F978-3-642-30284-8_50.pdf}, year = {2012} } @article{Chepelev2012, abstract = {Abstract Background The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest. Results To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development. Conclusions We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development.}, author = {Chepelev, Leonid L. and Hastings, Janna and Ennis, Marcus and Steinbeck, Christoph and Marcus Ennis, M. and Christoph Steinbeck, C. and Dumontier, Michel}, doi = {10.1186/1471-2105-13-3}, journal = {BMC Bioinformatics}, month = {jan}, title = {Self-organizing ontology of biochemically relevant small molecules}, url = {http://dx.doi.org/10.1186/1471-2105-13-3}, volume = {13}, year = {2012} } @article{Cruz-Toledo2012, abstract = {Over the past several decades, rapid developments in both molecular and information technology have collectively increased our ability to understand molecular recognition. One emerging area of interest in molecular recognition research includes the isolation of aptamers. Aptamers are single-stranded nucleic acid or amino acid polymers that recognize and bind to targets with high affinity and selectivity. While research has focused on collecting aptamers and their interactions, most of the information regarding experimental methods remains in the unstructured and textual format of peer reviewed publications. To address this, we present the Aptamer Base, a database that provides detailed, structured information about the experimental conditions under which aptamers were selected and their binding affinity quantified. The open collaborative nature of the Aptamer Base provides the community with a unique resource that can be updated and curated in a decentralized manner, thereby accommodating the ever evolving field of aptamer research.}, author = {Cruz-Toledo, Jose and McKeague, Maureen and Zhang, Xueru and Giamberardino, Amanda and McConnell, Erin and Francis, Tariq and DeRosa, Maria C. and Dumontier, Michel}, doi = {10.1093/database/bas006}, journal = {Database}, month = {jan}, title = {Aptamer base: a collaborative knowledge base to describe aptamers and SELEX experiments}, url = {http://dx.doi.org/10.1093/database/bas006}, volume = {2012}, year = {2012} } @article{dummy-Author_name2017, author = {dummy-Author_name, and Wolstencroft, Katy and McMurry, Julie A. and Blomberg, Niklas and Burdett, Tony and Mueller, Wolfgang and Conlin, Tom and Conte, Nathalie and Courtot, Melanie and Deck, John and Rc, Jimenez and Dumontier, Michel and Gonzalez-Beltran, Alejandra and Fellows, Donal K. and Gormanns, Philipp and Novère, Nicolas Le and Grethe, Jeffrey and Hastings, Janna and Juty, Navtej and Hermjakob, Henning and Hériché, Jean-Karim and Burdett, Anthony and Ison, Jon C. and Jimenez, Rafael C. and Jupp, Simon and Kunze, John and Laibe, Camille and Ja, McMurry and Morris, Chris and Malone, James Robert and Le Novere, Nicolas and Muilu, Juha and Martin, Maria-Jesus and Müller, Wolfgang and Rocca-Serra, Philippe and Sansone, Susanna-Assunta and Sariyar, Murat and Snoep, Jacky L. and Stanford, Natalie J. and Soiland-Reyes, Stian and Swainston, Neil and Burdett, T. and Washington, Nicole and Williams, Alan R. and Heriche, Jean-Karim and Wimalaratne, Sarala M. and Winfree, Lilly M. and Dk, Fellows and McEntyre, Jo and Jk, Hériché and Mungall, Christopher J. and Goble, Carole and Jc, Ison and Haendel, Melissa A. and Parkinson, Helen}, doi = {10.1371/journal.pbio.2001414}, journal = {PLoS Biology}, month = {mar}, pages = {e2001414}, title = {Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data}, url = {https://doi.org/10.1371/journal.pbio.2001414}, volume = {15}, year = {2017} } @article{Dumontier2012, author = {Dumontier, Michel and Wild, David J.}, doi = {10.1109/mic.2012.122}, journal = {IEEE Internet Computing}, month = {nov}, pages = {68-71}, title = {Linked Data in Drug Discovery}, url = {https://oadoi.org/10.1109/mic.2012.122}, volume = {16}, year = {2012} } @article{Duque-Ramos2013, abstract = {The increasing importance of ontologies has resulted in the development of a large number of ontologies in both coordinated and non-coordinated efforts. The number and complexity of such ontologies make hard to ontology and tool developers to select which ontologies to use and reuse. So far, there are no mechanism for making such decisions in an informed manner. Consequently, methods for evaluating ontology quality are required. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies. OQuaRE has been applied to identify the strengths and weaknesses of different ontologies but, so far, this framework has not been evaluated itself. Therefore, in this paper we present the evaluation of OQuaRE, performed by an international panel of experts in ontology engineering. The results include the positive and negative aspects of the current version of OQuaRE, the completeness and utility of the quality metrics included in OQuaRE and the comparison between the results of the manual evaluations done by the experts and the ones obtained by a software implementation of OQuaRE.}, author = {Duque-Ramos, Astrid and Fernández-Breis, Jesualdo Tomás and Iniesta-Moreno, Miguela and Dumontier, Michel and Aranguren, Mikel Egaña and Egaña Aranguren, Mikel and Schulze, Stefan and Schulz, Stefan and Aussenac-Gilles, Nathalie and Stevens, Robert}, doi = {10.1016/j.eswa.2012.11.004}, journal = {Expert Systems with Applications}, month = {jun}, pages = {2696-2703}, title = {Evaluation of the OQuaRE framework for ontology quality}, url = {https://hal.archives-ouvertes.fr/hal-01119497/file/Duque-Ramos_12991.pdf}, volume = {40}, year = {2013} } @article{Garcia2018, author = {Garcia, Alexander and Lopez, Federico and Garcia, Leyla and Giraldo, Olga and Bucheli, Victor and Dumontier, Michel}, doi = {10.7717/peerj.4201}, journal = {PeerJ}, month = {jan}, pages = {e4201}, title = {Biotea: semantics for Pubmed Central}, url = {https://doi.org/10.7717/peerj.4201}, volume = {6}, year = {2018} } @article{Garcia2020, author = {Garcia, Leyla and Batut, Bérénice and Burke, Melissa L. and Kuzak, Mateusz and Psomopoulos, Fotis and Arcila, Ricardo and Attwood, Teresa K. and Beard, Niall and Carvalho-Silva, Denise and Dimopoulos, Alexandros C. and del Angel, Victoria Dominguez and Dumontier, Michel and Gurwitz, Kim T. and Krause, Roland and McQuilton, Peter and Le Pera, Loredana and Morgan, Sarah L. and Rauste, Päivi and Via, Allegra and Kahlem, Pascal and Rustici, Gabriella and van Gelder, Celia W. G. and Palagi, Patricia M.}, doi = {10.1371/journal.pcbi.1007854}, journal = {PLoS Computational Biology}, month = {may}, pages = {e1007854}, title = {Ten simple rules for making training materials FAIR}, url = {https://doi.org/10.1371/journal.pcbi.1007854}, volume = {16}, year = {2020} } @article{Gawronski2011, author = {Gawronski, Alexander and Dumontier, Michel}, doi = {10.1016/j.cag.2011.03.039}, journal = {Computers and Graphics}, month = {aug}, pages = {823-830}, title = {MoSuMo: A Semantic Web service to generate electrostatic potentials across solvent excluded protein surfaces and binding pockets}, url = {https://oadoi.org/10.1016/j.cag.2011.03.039}, volume = {35}, year = {2011} } @article{Groza2012, abstract = {In this paper we explore the measurement of activity in ontology projects as an aspect of community ontology building. When choosing whether to use an ontology or whether to participate in its development, having some knowledge of how actively that ontology ...}, author = {Groza, Tudor and Tudorache, Tania and Dumontier, Michel}, doi = {10.1016/j.jbi.2012.11.007}, journal = {Journal of Biomedical Informatics}, month = {dec}, pages = {1-4}, title = {State of the art and open challenges in community-driven knowledge curation}, url = {https://www.researchgate.net/profile/Tania_Tudorache/publication/233877838_Commentary_State_of_the_art_and_open_challenges_in_community-driven_knowledge_curation/links/5640e50208aeacfd893605ed.pdf}, volume = {46}, year = {2012} } @article{Hastings2011, abstract = {Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA).}, author = {Hastings, Janna and Chepelev, Leonid and Willighagen, Egon and Adams, Nico and Steinbeck, Christoph and Dumontier, Michel and Michel Dumontier, M.}, doi = {10.1371/journal.pone.0025513}, journal = {PLoS ONE}, month = {oct}, pages = {e25513}, title = {The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web}, url = {https://doi.org/10.1371/journal.pone.0025513}, volume = {6}, year = {2011} } @article{Hoehndorf2012, author = {Hoehndorf, Robert and Dumontier, Michel and Gkoutos, Georgios V.}, doi = {10.1093/bioinformatics/bts350}, journal = {Bioinformatics}, month = {jun}, pages = {2169-2175}, title = {Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics}, url = {https://academic.oup.com/bioinformatics/article-pdf/28/16/2169/926095/bts350.pdf}, volume = {28}, year = {2012} } @article{Hoehndorf2012_2, abstract = {Ontologies are now pervasive in biomedicine, where they serve as a means to standardize terminology, to enable access to domain knowledge, to verify data consistency and to facilitate integrative analyses over heterogeneous biomedical data. For this purpose, research on biomedical ontologies applies theories and methods from diverse disciplines such as information management, knowledge representation, cognitive science, linguistics and philosophy. Depending on the desired applications in which ontologies are being applied, the evaluation of research in biomedical ontologies must follow different strategies. Here, we provide a classification of research problems in which ontologies are being applied, focusing on the use of ontologies in basic and translational research, and we demonstrate how research results in biomedical ontologies can be evaluated. The evaluation strategies depend on the desired application and measure the success of using an ontology for a particular biomedical problem. For many applications, the success can be quantified, thereby facilitating the objective evaluation and comparison of research in biomedical ontology. The objective, quantifiable comparison of research results based on scientific applications opens up the possibility for systematically improving the utility of ontologies in biomedical research.}, author = {Hoehndorf, Robert and Dumontier, Michel and Gkoutos, Georgios V.}, doi = {10.1093/bib/bbs053}, journal = {Briefings in Bioinformatics}, month = {sep}, pages = {696-712}, title = {Evaluation of research in biomedical ontologies}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22962340}, volume = {14}, year = {2012} } @article{Jagodnik2017, author = {Jagodnik, Kathleen M. and Koplev, Simon and Jenkins, Sherry L. and Ohno-Machado, Lucila and Paten, Benedict and Schurer, Stephan C. and Dumontier, Michel and Verborgh, Ruben and Bui, Alex and Ping, Peipei and McKenna, Neil J. and Madduri, Ravi and Pillai, Ajay and Ma'ayan, Avi}, doi = {10.1016/j.jbi.2017.05.006}, journal = {Journal of Biomedical Informatics}, month = {jul}, pages = {49-57}, title = {Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop}, url = {https://biblio.ugent.be/publication/8532703/file/8532705.pdf}, volume = {71}, year = {2017} } @article{Samwald2012, abstract = { Understanding how each individual’s genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients’ medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic Web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products. }, author = {Samwald, Matthias and Coulet, Adrien and Huerga, Iker and Powers, Robert L. and Luciano, Joanne S. and Freimuth, Robert R. and Whipple, Frederick and Pichler, Elgar and Prud'Hommeaux, Eric and Dumontier, Michel and Marshall, M. Scott and Scott Marshall, M.}, doi = {10.2217/pgs.11.179}, journal = {Pharmacogenomics}, month = {jan}, pages = {201-212}, title = {Semantically enabling pharmacogenomic data for the realization of personalized medicine.}, url = {https://doi.org/10.2217/pgs.11.179}, volume = {13}, year = {2012} } @article{Scott Marshall2016, abstract = {Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.}, author = {Scott Marshall, M. and Dumontier, Michel and Novère, Nicolas Le and Gray, Alasdair J. G. and Marshall, M. Scott and Ajg, Gray and Alexiev, Vladimir and Aj, Gray and Ansell, Peter and Ms, Marshall and Bader, Gary D. and Bolleman, Jerven T. and Baran, Joachim and Beltran, Alejandra N. Gonzalez and Callahan, Alison and Gombocz, Erich A. and Gonzalez Beltran, Alejandra N. and Gd, Bader and Cruz-Toledo, José and Groth, Paul and Gaudet, Pascale and Haendel, Melissa and Ito, Maori and Jupp, Simon and Jt, Bolleman and Juty, Nick and Katayama, Toshiaki and Kobayashi, Norio and An, Gonzalez Beltran and Krishnaswami, Kalpana and Laibe, Camille and Le Novère, Nicolas and Lin, Simon and Malone, James and Miller, Michael and Mungall, Christopher J. and Rietveld, Laurens and Wimalaratne, Sarala M. and Ea, Gombocz and Yamaguchi, Atsuko}, doi = {10.7717/peerj.2331}, journal = {PeerJ}, month = {jan}, pages = {e2331}, title = {The health care and life sciences community profile for dataset descriptions}, url = {https://doi.org/10.7717/peerj.2331}, volume = {4}, year = {2016} } @misc{Tenenbaum2018, author = {Tenenbaum, Jessica and Sansone, Susanna-Assunta and Schriml, Lynn and Rustici, Gabriella and Schurer, Stephan and Sharples, Kathryn and Rocca-Serra, Philippe and Soares e. Silva, Marina and Stanford, Natalie J. and Subirats-Coll, Inmaculada and Swedlow, Jason and Tong, Weida and McQuilton, Peter and Wilkinson, Mark and Wise, John and Hodson, Simon and Gonzalez-Beltran, Alejandra and Lawrence, Rebecca and Thurston, Milo and Khodiyar, Varsha and Axton, J. Myles and Ball, Michael and Izzo, Massimiliano and Besson, Sebastien and Bloom, Theodora and Bonazzi, Vivien and Lister, Allyson and Jimenez, Rafael and Carr, David and Chan, Wei Mun and Chung, Caty and Clement-Stoneham, Geraldine and Cousijn, Helena and Dayalan, Saravanan and Batista, Dominique and Dumontier, Michel and Dzale Yeumo, Esther and Edmunds, Scott and Everitt, Nicholas and Granell, Ramon and Yilmaz, Pelin and Fripp, Dom and Goble, Carole and Golebiewski, Martin and Hall, Neil and Adekale, Melanie and Hanisch, Robert and Hucka, Michael and Huerta, Michael and Dauga, Delphine and Kenall, Amye and Kiley, Robert and Klenk, Juergen and Koureas, Dimitrios and Larkin, Jennie and Ganley, Emma and Lemberger, Thomas and Lynch, Nick and Ma'ayan, Avi and MacCallum, Catriona and Mons, Barend and Moore, Josh and Muller, Wolfgang and Murray, Hollydawn and Nobusada, Tomoe and Noesgaard, Daniel and Paxton-Boyd, Jennifer and Orchard, Sandra and Rn, Lawrence}, month = {jan}, title = {FAIRsharing, a cohesive community approach to the growth in standards, repositories and policies}, year = {2018} } @article{Vandervalk2013, author = {Vandervalk, Ben and Luke McCarthy, E. and Cruz-Toledo, José and Klein, Artjom and Baker, Christopher J. O. and Dumontier, Michel and Wilkinson, Mark D.}, doi = {10.2196/resprot.2315}, journal = {JMIR Research Protocols}, month = {apr}, pages = {e14}, title = {The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions}, url = {https://doi.org/10.2196/resprot.2315}, volume = {2}, year = {2013} } @article{Wilkinson2016, abstract = {Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, or EUDat). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved even to the level of an individual spreadsheet cell. We note that the behaviors of this architecture compare favorably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.}, author = {Wilkinson, Mark D. and da Silva Santos, Luiz Olavo Bonino and van Mulligen, Erik M. and Md, Wilkinson and van Ciccarese, Paolo and Verborgh, Ruben and Bonino da Silva Santos, Luiz Olavo and Lo, Bonino da Silva Santos and Clark, Tim and Swertz, Morris A. and Kelpin, Fleur D. L. and Gray, Alasdair J. G. and Ma, Swertz and Schultes, Erik A. and Fd, Kelpin and Ajg, Gray and Mulligen, Erik M. and Ea, Schultes and Ciccarese, Paolo and Gavai, Anand and Em, van Mulligen and Thompson, Mark and Kuzniar, Arnold and Kaliyaperumal, Rajaram and Bolleman, Jerven T. and Dumontier, Michel and Others, }, doi = {10.7287/peerj.preprints.2522v1}, journal = {PeerJ Computer Science}, month = {jan}, pages = {e110}, title = {Interoperability and FAIRness through a novel combination of Web technologies}, url = {https://doi.org/10.7717/peerj-cs.110}, volume = {3}, year = {2016} } @article{Zhu2016, abstract = {AbstractRheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.}, author = {Zhu, Jun and de Vries, Niek and Bonet, Jaume and Sieberts, Solveig K. and Sk, Sieberts and Vert, Jean-Philippe and Balagurusamy, Venkat S. K. and García-García, Javier and Zhu, Fan and Marín, Manuel Alejandro and Planas-Iglesias, Joan and Poglayen, Daniel and Panwar, Bharat and Stahl, Eli and Cui, Jing and Suver, Christine and Falcao, Andre O. and Cheng, Lu and Pratap, Abhishek and Hoff, Bruce and Dillenberger, Donna and Neto, Elias Chaibub and Pandey, Gaurav and Norman, Thea and Aittokallio, Tero and Ammad-Ud-Din, Muhammad and Azencott, Chloe-Agathe and Bellón, Víctor and Pappas, Dimitrios and Boeva, Valentina and Bunte, Kerstin and Eksi, Ridvan and Chheda, Himanshu and Corander, Jukka and Aguilar, Daniel and Dumontier, Michel and Fornés, Oriol and Goldenberg, Anna and Gopalacharyulu, Peddinti and Bonet, Bo and Hajiloo, Mohsen and Anton, Bernat and Guney, Emre and Hidru, Daniel and Jaiswal, Alok and Pirinen, Matti and Kaski, Samuel and Li, Hongdong and Saarela, Janna and Khalfaoui, Beyrem and Khan, Suleiman Ali and Samwald, Matthias and Kramer, Eric R. and Stoven, Véronique and Marttinen, Pekka and Tang, Hao and Mezlini, Aziz M. and Tang, Jing and Molparia, Bhuvan and Torkamani, Ali and Wang, Bo and Wang, Tao and Wennerberg, Krister and Wineinger, Nathan E. and Xiao, Guanghua and Bonet, J. and Calaza, Manuel and Xie, Yang and Yeung, Rae and Elmarakeby, Haitham and Zhan, Xiaowei and Savage, Richard S. and Zhao, Cheng and Heath, Lenwood S. and Long, Quan and Moore, Jonathan D. and Balagurusamy, S. and Opiyo, Stephen Obol and Greenberg, Jeff and Kremer, Joel and Michaud, Kaleb and Barton, Anne and Shadick, Nancy and Coenen, Marieke and Mariette, Xavier and Weinblatt, Michael and Miceli, Corinne and Tak, Paul P. and Gerlag, Danielle and Cheng, Cheng and Huizinga, Tom W. J. and Kurreeman, Fina and Louis Bridges Jr., S. and Allaart, Cornelia F. and Criswell, Lindsey and Saevarsdottir, Saedis and Moreland, Larry and Klareskog, Lars and Guan, Yuanfang and Plenge, Robert and Padyukov, Leonid and Ma, Marín and Gregersen, Peter K. and Stolovitzky, Gustavo and Friend, Stephen and Guan, Guanghua and Oliva, Baldo and Bridges, S. Louis and Lm, Mangravite and Mangravite, Lara M. and Peddinti, Gopal}, doi = {10.1038/ncomms12460}, journal = {Nature Communications}, month = {aug}, title = {Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.}, url = {http://dx.doi.org/10.1038/ncomms12460}, volume = {7}, year = {2016} }