Biological Data Mining in Protein Interaction Networks

by Xiao-Li Li (Editor), See-Kiong Ng (Editor)

Overview

Methods for detecting protein-protein interactions (PPIs) have given researchers a global picture of protein interactions on a genomic scale. Biological Data Mining in Protein Interaction Networks explains bioinformatic methods for predicting PPIs, as well as data mining methods to mine or analyze various protein interaction networks. A defining body of research within the field, this book discovers underlying interaction mechanisms by studying intra-molecular features that form the common denominator of various PPIs.

Product Details

ISBN-13:	9781605663982
Publisher:	Medical Information Science Reference
Publication date:	05/31/2009
Series:	Premier Reference Source
Pages:	450
Product dimensions:	8.70(w) x 11.30(h) x 1.30(d)

Editorial Reviews

Reviewer: Omer Iqbal, MD, FACC, FESC (Loyola University Medical Center)
Description: This well-illustrated book explains bioinformatic methods for predicting protein-protein interactions and provides the inner working mechanisms of cells leading to the understanding of underlying disease pathways and discovery of new drugs to benefit mankind.
Purpose: The purpose is to disseminate research results and best practice from cross-disciplinary researchers and practitioners interested in and working on bioinformatics, data mining, and proteomics.
Audience: The book targets researchers in the areas of bioinformatics, data mining, machine learning, and data structure. It also is intended for practitioners in the industry in these areas and for instructors and postgraduate students in colleges and universities. The authors are recognized authorities in this field.
Features: The first of the book's five sections is constituted of an introduction and two chapters on the molecular biology of protein-protein interactions for computer scientists and data mining for biologists. The second section details the protein-protein interaction network connection and cleaning as well as various aspects of protein-protein interactions. The third section explores knowledge discovery from protein-protein interaction networks and the fourth section explains biological applications using protein-protein interaction analysis. The last section on tools for analysis of protein-protein interaction networks covers graphical analysis and visualization and module finding for protein-protein interactions as well as network querying techniques for protein-protein interaction network comparison. The detailed table of contents is helpful. The use of color in different illustrations may have enhanced clarity and helped comprehension.
Assessment: This is a valuable resource for researchers, biologists, computer scientists, postgraduate students, and others interested in the field of biological data mining in protein-protein interaction networks that will help illuminate the inner working mechanisms of cells, thereby enabling understanding of underlying disease pathways and discovery of new drugs.

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