The potential of translational bioinformatics approaches for pharmacology research.
暂无分享,去创建一个
[1] Ryen W. White,et al. Web-scale pharmacovigilance: listening to signals from the crowd , 2013, J. Am. Medical Informatics Assoc..
[2] Yang Xiang,et al. Efficiently mining Adverse Event Reporting System for multiple drug interactions , 2014, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.
[3] Maria F. Sassano,et al. Automated design of ligands to polypharmacological profiles , 2012, Nature.
[4] Fergus J Couch,et al. Pharmacogenetics of tamoxifen biotransformation is associated with clinical outcomes of efficacy and hot flashes. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[5] Adrian Benton,et al. Online discussion of drug side effects and discontinuation among breast cancer survivors , 2013, Pharmacoepidemiology and drug safety.
[6] P Ryan,et al. Novel Data‐Mining Methodologies for Adverse Drug Event Discovery and Analysis , 2012, Clinical pharmacology and therapeutics.
[7] E. Shapiro,et al. Single-cell sequencing-based technologies will revolutionize whole-organism science , 2013, Nature Reviews Genetics.
[8] Melissa A. Basford,et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. , 2010, American journal of human genetics.
[9] A. Butte,et al. Predicting Adverse Drug Reactions Using Publicly Available PubChem BioAssay Data , 2011, Clinical pharmacology and therapeutics.
[10] Russ B. Altman,et al. Discovery and Explanation of Drug-Drug Interactions via Text Mining , 2011, Pacific Symposium on Biocomputing.
[11] Hua Xu,et al. Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin , 2011, J. Am. Medical Informatics Assoc..
[12] Xu Han,et al. Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records: Novel Myopathy Associated Drug Interactions , 2012, PLoS Comput. Biol..
[13] B D Athey,et al. Needs for an Expanded Ontology‐Based Classification of Adverse Drug Reactions and Related Mechanisms , 2012, Clinical pharmacology and therapeutics.
[14] S Sarntivijai,et al. Use of Internet Search Logs to Evaluate Potential Drug Adverse Events , 2014, Clinical pharmacology and therapeutics.
[15] Russ B. Altman,et al. Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text , 2009, BMC Bioinformatics.
[16] Melissa A. Basford,et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.
[17] Xu Han,et al. An integrated pharmacokinetics ontology and corpus for text mining , 2013, BMC Bioinformatics.
[18] Michael Q. Zhang,et al. Network-based global inference of human disease genes , 2008, Molecular systems biology.
[19] D. Madigan,et al. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership , 2012, Statistics in medicine.
[20] Xiaoyan Wang,et al. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. , 2009, Journal of the American Medical Informatics Association : JAMIA.
[21] R. Altman,et al. Personal Genomic Measurements: The Opportunity for Information Integration , 2013, Clinical pharmacology and therapeutics.
[22] Carol Friedman,et al. Statistical Mining of Potential Drug Interaction Adverse Effects in FDA's Spontaneous Reporting System. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.
[23] Lang Li,et al. Exploring a structural protein-drug interactome for new therapeutics in lung cancer. , 2014, Molecular bioSystems.
[24] W. Bilker,et al. Pharmacoepidemiologic and in vitro evaluation of potential drug-drug interactions of sulfonylureas with fibrates and statins. , 2014, British journal of clinical pharmacology.
[25] S. Gabriel,et al. Advances in understanding cancer genomes through second-generation sequencing , 2010, Nature Reviews Genetics.
[26] J. Overhage,et al. Advancing the Science for Active Surveillance: Rationale and Design for the Observational Medical Outcomes Partnership , 2010, Annals of Internal Medicine.
[27] R. Altman,et al. Integrating systems biology sources illuminates drug action , 2014, Clinical pharmacology and therapeutics.
[28] Cui Tao,et al. OAE: The Ontology of Adverse Events , 2014, J. Biomed. Semant..
[29] R. Altman. Translational Bioinformatics: Linking the Molecular World to the Clinical World , 2012, Clinical pharmacology and therapeutics.
[30] Jeffrey R. Whiteaker,et al. Proteogenomic characterization of human colon and rectal cancer , 2014, Nature.
[31] Bo Zhang,et al. A Network Pharmacology Approach to Determine Active Compounds and Action Mechanisms of Ge-Gen-Qin-Lian Decoction for Treatment of Type 2 Diabetes , 2014, Evidence-based complementary and alternative medicine : eCAM.
[32] Ruth Nussinov,et al. Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review , 2012, Pharmacology & therapeutics.
[33] Roded Sharan,et al. Large-Scale Elucidation of Drug Response Pathways in Humans , 2012, J. Comput. Biol..
[34] Melissa A. Basford,et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future , 2013, Genetics in Medicine.
[35] Christopher G. Chute,et al. The National Center for Biomedical Ontology , 2012, J. Am. Medical Informatics Assoc..
[36] Lyle H. Ungar,et al. Identifying potential adverse effects using the web: A new approach to medical hypothesis generation , 2011, J. Biomed. Informatics.
[37] Russ B. Altman,et al. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports , 2012, J. Am. Medical Informatics Assoc..
[38] Roded Sharan,et al. PRINCIPLE: a tool for associating genes with diseases via network propagation , 2011, Bioinform..