Semantic Breakthrough in Drug Discovery por David Wild

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Semantic Breakthrough in Drug Discovery David Wild


David Wild



The current drug development paradigm—sometimes expressed as, “One disease, one target, one drug”—is under question, as relatively few drugs have reached the market in the last two decades. Meanwhile, the research focus of drug discovery is being placed on the study of drug action on biological systems as a whole, rather than on individual components of such systems. The vast amount of biological information about genes and proteins and their modulation by small molecules is pushing drug discovery to its next critical steps, involving the integration of chemical knowledge with these biological databases. Systematic integration of these heterogeneous datasets and the provision of algorithms to mine the integrated datasets would enable investigation of the complex mechanisms of drug action; however, traditional approaches face challenges in the representation and integration of multi-scale datasets, and in the discovery of underlying knowledge in the integrated datasets. The Semantic Web, envisioned to enable machines to understand and respond to complex human requests and to retrieve relevant, yet distributed, data, has the potential to trigger system-level chemical-biological innovations. Chem2Bio2RDF is presented as an example of utilizing Semantic Web technologies to enable intelligent analyses for drug discovery.Table of Contents: Introduction / Data Representation and Integration Using RDF / Data Representation and Integration Using OWL / Finding Complex Biological Relationships in PubMed Articles using Bio-LDA / Integrated Semantic Approach for Systems Chemical Biology Knowledge Discovery / Semantic Link Association Prediction / Conclusions / References / Authors’ Biographies

Acerca de David Wild

David Wild is an Associate Professor at Indiana University’s School of Informatics and Computing (SOIC) where he is a Graduate Program Director for the new Data Science Program and leads the Cheminformatics and Chemogenomics Research Group (CCRG). He is the founder and Chief Executive Officer at the data science technology company Data2Discovery Inc. and consults through Wild Consulting and Innovations. He has around 100 research publications, edits the Journal of Cheminformatics, and maintains a variety of educational resources including Learn Cheminformatics site, Surviving Disasters site and the All Hazards Blog. He is an Emergency Medical Technician (EMT) and volunteer with Bloomington Township Fire Department and Argus Canine Search and Rescue.

Acerca de Ying Ding

Dr. Ying Ding is an Associate Professor at School of Informatics and Computing, Indiana University. Previously, she worked as a senior researcher at the University of Innsbruck, Austria and as a researcher at the Free University of Amsterdam, the Netherlands and has been involved in various NIH and European-Union funded Semantic Web projects and has published 170+ papers in journals, conferences, and workshops. She is the co-editor of book series Semantic Web Synthesis by Morgan & Claypool Publisher. She is co-author of the book Intelligent Information Integration in B2B Electronic Commerce, published by Kluwer Academic Publishers, and co-author of book chapters in the book Spinning the Semantic Web, published by MIT Press, and Towards the Semantic Web: Ontology-driven Knowledge Management, published by Wiley. Dr. Ding is on the editorial board of four ISI indexed top journals in Information Science and Semantic Web. Her current research interest areas include social network analysis, Semantic Web, citation analysis, knowledge management, and application of Web Technology.

Acerca de Bin Chen

Bin Chen is currently a postdoctoral scholar at Stanford University. His primary interest is to develop tools and algorithms to identify new therapeutics from publicly available data sources. He received his Ph.D. in informatics at Indiana University, Bloomington. During his graduate school, he primarily developed three systems (i.e., Chem2Bio2RDF, Chem2Bio2OWL and SLAP) used to represent, integrate and mine semantic data for drug discovery. He interned in three pharmaceutical companies (i.e., Novartis, Pfizer and Merck) for two years during this graduate school. He has published over 20 scientific papers.

Acerca de Huijun Wang

Huijun Wang is an Associate Principle Scientist in the Cheminformatics Department at Merck. She leads the competitive intelligence data integration and information retrieval project and also focuses on text mining and data mining of chemical and biological information related to drug discovery. She has worked in the pharmaceutical industry for many years, including four years at Pfizer before joining Merck. She managed the large-scale internal and external data integration, retrieval and mining effects. She graduated from Indiana University with a Ph.D. in Informatics and an M.S. in Computer Science and Cheminformatics.

Datos del libro
Morgan & Claypool Publishers 2014
ISBN: 9781627054515
Idioma: Español
Formatos: pdf epub kindle mobi

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