Semantic Breakthrough in Drug Discovery

Semantic Breakthrough in Drug Discovery

Bin Chen, Huijun Wang, Ying Ding, David Wild
ISBN: 9781627054508 | PDF ISBN: 9781627054515
Copyright © 2015 | 142 Pages | Publication Date: 10/01/2014

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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

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

About the Author(s)

Bin Chen, Stanford University

Huijun Wang, Merck

Ying Ding, Indiana University

David Wild, Indiana University

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