Text-mining solutions for biomedical research: enabling integrative biology
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[1] M. DePamphilis,et al. HUMAN DISEASE , 1957, The Ulster Medical Journal.
[2] D. Swanson. Medical literature as a potential source of new knowledge. , 1990, Bulletin of the Medical Library Association.
[3] Marti A. Hearst. Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.
[4] Marti A. Hearst. Untangling Text Data Mining , 1999, ACL.
[5] Miguel A. Andrade-Navarro,et al. Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions , 1999, ISMB.
[6] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[7] Stefan Decker,et al. Creating Semantic Web Contents with Protégé-2000 , 2001, IEEE Intell. Syst..
[8] T. Jenssen,et al. A literature network of human genes for high-throughput analysis of gene expression , 2001, Nature Genetics.
[9] A. Valencia,et al. Mining functional information associated with expression arrays , 2001, Functional & Integrative Genomics.
[10] P. Bork,et al. Association of genes to genetically inherited diseases using data mining , 2002, Nature Genetics.
[11] Mikhail V. Blagosklonny,et al. Conceptual biology: Unearthing the gems , 2002, Nature.
[12] Joel D. Martin,et al. PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine , 2003, BMC Bioinformatics.
[13] Terri K. Attwood,et al. PRINTS and its automatic supplement, prePRINTS , 2003, Nucleic Acids Res..
[14] Gregory D. Schuler,et al. Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.
[15] Lars Juhl Jensen,et al. Large-scale extraction of gene regulation for model organisms in an ontological context , 2004, Silico Biol..
[16] Michael Krauthammer,et al. Term identification in the biomedical literature , 2004, J. Biomed. Informatics.
[17] Hector J. Levesque,et al. Knowledge Representation and Reasoning , 2004 .
[18] Hans-Michael Müller,et al. Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature , 2004, PLoS biology.
[19] Hector J. Levesque,et al. Chapter 14 – Actions , 2004 .
[20] Mark I McCarthy,et al. Progress in defining the molecular basis of type 2 diabetes mellitus through susceptibility-gene identification. , 2004, Human molecular genetics.
[21] P. Bork,et al. G2D: a tool for mining genes associated with disease , 2005, BMC Genetics.
[22] Michael Schroeder,et al. GoPubMed: exploring PubMed with the Gene Ontology , 2005, Nucleic Acids Res..
[23] K. E. Ravikumar,et al. Beyond the clause: extraction of phosphorylation information from medline abstracts , 2005, ISMB.
[24] Ralf Zimmer,et al. Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts , 2005, ECCB/JBI.
[25] Alfonso Valencia,et al. Implementing the iHOP concept for navigation of biomedical literature , 2005, ECCB/JBI.
[26] Shawn M. Douglas,et al. PubNet: a flexible system for visualizing literature derived networks , 2005, Genome Biology.
[27] P. Bork,et al. Literature mining for the biologist: from information retrieval to biological discovery , 2006, Nature Reviews Genetics.
[28] Andrey Rzhetsky,et al. Microparadigms: chains of collective reasoning in publications about molecular interactions. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[29] See-Kiong Ng,et al. BioContrasts: extracting and exploiting protein-protein contrastive relations from biomedical literature , 2005, Bioinform..
[30] Andy Seaborne,et al. SWAN: A distributed knowledge infrastructure for Alzheimer disease research , 2006, J. Web Semant..
[31] K. E. Ravikumar,et al. An online literature mining tool for protein phosphorylation , 2006, Bioinform..
[32] Zhiyong Lu,et al. OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression , 2008, BMC Bioinformatics.
[33] M. Ashburner,et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.
[34] Ted Briscoe,et al. Natural Language Processing in aid of FlyBase curators , 2008, BMC Bioinformatics.
[35] Eric K. Neumann,et al. Knowledge networks in the age of the Semantic Web , 2007, Briefings Bioinform..
[36] A. Barabasi,et al. The human disease network , 2007, Proceedings of the National Academy of Sciences.
[37] Midori A. Harris,et al. BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm112 Databases and ontologies OBO-Edit—an ontology editor for biologists , 2007 .
[38] Julio Collado-Vides,et al. Automatic reconstruction of a bacterial regulatory network using Natural Language Processing , 2007, BMC Bioinformatics.
[39] David S. Wishart,et al. Nucleic Acids Research Polysearch: a Web-based Text Mining System for Extracting Relationships between Human Diseases, Genes, Mutations, Drugs Polysearch: a Web-based Text Mining System for Extracting Relationships between Human Diseases, Genes, Mutations, Drugs and Metabolites , 2008 .
[40] Hagit Shatkay,et al. Pacific Symposium on Biocomputing 13:604-615(2008) EPILOC: A (WORKING) TEXT-BASED SYSTEM FOR PREDICTING PROTEIN SUBCELLULAR LOCATION , 2022 .
[41] Michael Schroeder,et al. Inter-species normalization of gene mentions with GNAT , 2008, ECCB.
[42] Son Doan,et al. BioCaster: detecting public health rumors with a Web-based text mining system , 2008, Bioinform..
[43] Maurice Bouwhuis,et al. CoPub: a literature-based keyword enrichment tool for microarray data analysis , 2008, Nucleic Acids Res..
[44] Dietrich Rebholz-Schuhmann,et al. Assessment of disease named entity recognition on a corpus of annotated sentences , 2008, BMC Bioinformatics.
[45] D. Vitkup,et al. Network properties of genes harboring inherited disease mutations , 2008, Proceedings of the National Academy of Sciences.
[46] Graciela Gonzalez,et al. BANNER: An Executable Survey of Advances in Biomedical Named Entity Recognition , 2007, Pacific Symposium on Biocomputing.
[47] Michael R. Seringhaus,et al. Seeking a New Biology through Text Mining , 2008, Cell.
[48] Sophia Ananiadou,et al. FACTA: a text search engine for finding associated biomedical concepts , 2008, Bioinform..
[49] Dietrich Rebholz-Schuhmann,et al. Categorization of services for seeking information in biomedical literature: a typology for improvement of practice , 2008, Briefings Bioinform..
[50] A. Valencia,et al. Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge , 2008, Genome Biology.
[51] Boris Motik,et al. OWL 2: The next step for OWL , 2008, J. Web Semant..
[52] Dietrich Rebholz-Schuhmann,et al. MedEvi: Retrieving textual evidence of relations between biomedical concepts from Medline , 2008, Bioinform..
[53] L. Grivell,et al. Text mining for biology - the way forward: opinions from leading scientists , 2008, Genome Biology.
[54] Nicole Tourigny,et al. Bio2RDF: Towards a mashup to build bioinformatics knowledge systems , 2008, J. Biomed. Informatics.
[55] Dietrich Rebholz-Schuhmann,et al. Text processing through Web services: calling Whatizit , 2008, Bioinform..
[56] K. Bretonnel Cohen,et al. Getting Started in Text Mining , 2008, PLoS Comput. Biol..
[57] Jun'ichi Tsujii,et al. New challenges for text mining: mapping between text and manually curated pathways , 2008, BMC Bioinformatics.
[58] P. Bork,et al. Drug Target Identification Using Side-Effect Similarity , 2008, Science.
[59] Dietrich Rebholz-Schuhmann,et al. Integrating protein-protein interactions and text mining for protein function prediction , 2008, BMC Bioinformatics.
[60] Catia Pesquita,et al. Metrics for GO based protein semantic similarity: a systematic evaluation , 2008, BMC Bioinformatics.
[61] Ken E. Whelan,et al. The Automation of Science , 2009, Science.
[62] Udo Hahn,et al. High-performance gene name normalization with GENO , 2009, Bioinform..
[63] S. O’Rahilly,et al. Human genetics illuminates the paths to metabolic disease , 2009, Nature.
[64] Daniel L. Rubin,et al. Comparison of concept recognizers for building the Open Biomedical Annotator , 2009, BMC Bioinformatics.
[65] Lawrence Hunter,et al. Biomedical Discovery Acceleration, with Applications to Craniofacial Development , 2009, PLoS Comput. Biol..
[66] Peter L. Elkin,et al. BioProspecting: novel marker discovery obtained by mining the bibleome , 2009, BMC Bioinformatics.
[67] Michael Kuhn,et al. Reflect: augmented browsing for the life scientist , 2009, Nature Biotechnology.
[68] Dietrich Rebholz-Schuhmann,et al. Annotation of protein residues based on a literature analysis: cross-validation against UniProtKb , 2009, BMC Bioinformatics.
[69] Judith A. Blake,et al. Integrating text mining into the MGI biocuration workflow , 2009, Database J. Biol. Databases Curation.
[70] Martijn J. Schuemie,et al. Novel Protein-Protein Interactions Inferred from Literature Context , 2009, PloS one.
[71] Taehoon Kim,et al. Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS , 2009, DTMBIO.
[72] Monte Westerfield,et al. Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation , 2009, PLoS biology.
[73] Natalya F. Noy,et al. BioPortal: Ontologies and Integrated Data Resources at the Click of a Mouse , 2009 .
[74] Mark A. Musen,et al. The Open Biomedical Annotator , 2009, Summit on translational bioinformatics.
[75] Goran Nenadic,et al. LINNAEUS: A species name identification system for biomedical literature , 2010, BMC Bioinformatics.
[76] Russ B. Altman,et al. Pharmacogenomics and bioinformatics: PharmGKB. , 2010, Pharmacogenomics.
[77] Peer Bork,et al. Ontologies in Quantitative Biology: A Basis for Comparison, Integration, and Discovery , 2010, PLoS biology.
[78] K. Bretonnel Cohen,et al. The structural and content aspects of abstracts versus bodies of full text journal articles are different , 2010, BMC Bioinformatics.
[79] David M Nathan,et al. Individualizing therapies in type 2 diabetes mellitus based on patient characteristics: what we know and what we need to know. , 2010, The Journal of clinical endocrinology and metabolism.
[80] Russ B. Altman,et al. Author ' s personal copy Using text to build semantic networks for pharmacogenomics , 2010 .
[81] Dietrich Rebholz-Schuhmann,et al. Improving the extraction of complex regulatory events from scientific text by using ontology-based inference , 2011, Semantic Mining in Biomedicine.
[82] Lynette Hirschman,et al. The FEBS Letters/BioCreative II.5 experiment: making biological information accessible , 2010, Nature Biotechnology.
[83] Livia Perfetto,et al. MINT, the molecular interaction database: 2009 update , 2009, Nucleic Acids Res..
[84] P. Bork,et al. A side effect resource to capture phenotypic effects of drugs , 2010, Molecular systems biology.
[85] Steve Pettifer,et al. Utopia documents: linking scholarly literature with research data , 2010, Bioinform..
[86] Holger Stenzhorn,et al. Establishing a distributed system for the simple representation and integration of diverse scientific assertions , 2010, J. Biomed. Semant..
[87] Junichi Tsujii,et al. Event extraction for systems biology by text mining the literature. , 2010, Trends in biotechnology.
[88] Dietrich Rebholz-Schuhmann,et al. UKPMC: a full text article resource for the life sciences , 2011, Nucleic Acids Res..
[89] R. Luben,et al. Genetic predisposition to obesity leads to increased risk of type 2 diabetes , 2011, Diabetologia.
[90] D. Rebholz-Schuhmann,et al. Diversity in the Interactions of Isoforms Linked to Clustered Transcripts: A Systematic Literature Analysis , 2011 .
[91] Robert J. Smith,et al. Personalized medicine in diabetes. , 2011, Clinical chemistry.
[92] Egon L. Willighagen,et al. OSCAR4: a flexible architecture for chemical text-mining , 2011, J. Cheminformatics.
[93] Christian Herder,et al. Genetics of type 2 diabetes: pathophysiologic and clinical relevance , 2011, European journal of clinical investigation.
[94] Nophar Geifman,et al. Towards an Age-Phenome Knowledge-base , 2011, BMC Bioinformatics.
[95] Mark D. Wilkinson,et al. The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation , 2011 .
[96] Paul N. Schofield,et al. PhenomeNET: a whole-phenome approach to disease gene discovery , 2011, Nucleic acids research.
[97] Alfonso Valencia,et al. How to link ontologies and protein–protein interactions to literature: text-mining approaches and the BioCreative experience , 2012, Database J. Biol. Databases Curation.
[98] María Martín,et al. The Gene Ontology: enhancements for 2011 , 2011, Nucleic Acids Res..
[99] R. Pietrobon,et al. Turning Text into Research Networks: Information Retrieval and Computational Ontologies in the Creation of Scientific Databases , 2012, PloS one.
[100] Damian Smedley,et al. MouseFinder: Candidate disease genes from mouse phenotype data , 2012, Human mutation.
[101] Russ B. Altman,et al. Discovery and Explanation of Drug-Drug Interactions via Text Mining , 2011, Pacific Symposium on Biocomputing.
[102] Dietrich Rebholz-Schuhmann,et al. Automatic recognition of conceptualization zones in scientific articles and two life science applications , 2012, Bioinform..
[103] D. Cooper,et al. Microattribution and nanopublication as means to incentivize the placement of human genome variation data into the public domain , 2012, Human mutation.
[104] K. Bretonnel Cohen,et al. Text mining for the biocuration workflow , 2012, Database J. Biol. Databases Curation.
[105] Goran Nenadic,et al. Towards semi-automated curation: using text mining to recreate the HIV-1, human protein interaction database , 2012, Database J. Biol. Databases Curation.
[106] Anne E. Trefethen,et al. Toward interoperable bioscience data , 2012, Nature Genetics.
[107] Huajun Chen,et al. Semantic Web meets Integrative Biology: a survey , 2013, Briefings Bioinform..