Knowledge-Driven, Data-Assisted Integrative Pathway Analytics

Knowledge-Driven, Data-Assisted Integrative Pathway Analytics

Padmalatha S. Reddy (Pfizer, USA), Stuart Murray (Agios Pharmaceuticals Inc, USA) and Wei Liu (Agios Pharmaceuticals Inc, USA)
Copyright: © 2013 |Pages: 22
DOI: 10.4018/978-1-4666-3604-0.ch009
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Abstract

Target and biomarker selection in drug discovery relies extensively on the use of various genomics platforms. These technologies generate large amounts of data that can be used to gain novel insights in biology. There is a strong need to mine these information-rich datasets in an effective and efficient manner. Pathway and network based approaches have become an increasingly important methodology to mine bioinformatics datasets derived from ‘omics’ technologies. These approaches also find use in exploring the unknown biology of a disease or functional process. This chapter provides an overview of pathway databases and network tools, network architecture, text mining and existing methods used in knowledge-driven data analysis. It shows examples of how these databases and tools can be used integratively to apply existing knowledge and network-based approach in data analytics.
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Introduction

Target and Biomarker Selection in Drug Discovery

A critical step in the drug discovery process is the effective selection of candidate molecular targets. Target identification and selection requires a thorough understanding of the cellular role of the target, the signaling and metabolic pathways it is involved in, and the network of interactions that are involved in the functional role of the target. Perturbations in one or more of these may be responsible for a disease state or an off-target effect during drug treatment. Companies must deploy effective methods to select the targets since the drug discovery and development process is expensive and time-consuming. Furthermore, it is essential to fully understand the target and disease pathways to minimize expensive late-stage failures and to successfully translate animal models into the clinical development of therapies. With the advent of high-throughput ‘omics’ technologies and the rise of informatics technologies, it has become possible to routinely and systematically explore targets and disease-related cellular pathways, as well as cross-talk between pathways and interaction networks. Thus, a rational pathway and network based approach for target and biomarker identification has begun to be adopted by pharmaceutical companies in the recent years.

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