Bioinformatics: Volume II: Structure, Function, and Applications(1526 Methods in Molecular Biology)

This second edition provides updated and expanded chapters covering a broad sampling of useful and current methods in the rapidly developing and expanding field of bioinformatics. Bioinformatics, Volume II: Structure, Function, and Applications, Second Edition is comprised of three sections: Structure, Function, Pathways and Networks; Applications; and Computational Methods. The first section examines methodologies for understanding biological molecules as systems of interacting elements. The Applications section covers numerous applications of bioinformatics, focusing on analysis of genome-wide association data, computational diagnostic, and drug discovery. The final section describes four broadly applicable computational methods that are important to this field. These are: modeling and inference, clustering, parameterized algorithmics, and visualization. As a volume in the highly successful Methods in Molecular Biology series, chapters feature the kind of detailand expert implementation advice to ensure positive results. Comprehensive and practical, Bioinformatics, Volume II: Structure, Function, and Applications is an essential resource for graduate students, early career researchers, and others who are in the process of integrating new bioinformatics methods into their research.

Table of Contents:
3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data.- Inferring Function from Homology.- Inferring Functional Relationships from Conservation of Gene Order.- Structural and Functional Annotation of Long Non-Coding RNAs.- Construction of Functional Gene Networks Using Phylogenetic Profiles.- Inferring Genome-Wide Interaction Networks.- Integrating Heterogeneous Datasets for Cancer Module Identification.- Metabolic Pathway Mining.- Analysis of Genome-Wide Association Data.- Adjusting for Familial Relatedness in the Analysis of GWAS Data.- Analysis of Quantitative Trait Loci.- High-Dimensional Profiling for Computational Diagnosis.- Molecular Similarity Concepts for Informatics Applications.- Compound Data Mining for Drug Discovery.- Studying Antibody Repertoires with Next-Generation Sequencing.- Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures.- Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.- Inference Method for Developing Mathematical Models of Cell Signaling Pathways Using Proteomic Datasets.- Clustering.- Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.- Information Visualization for Biological Data.

Review :
“The book is as an excellent starting point for a wide audience including undergraduates, graduates and established researchers alike. The amount of detail presented for each methodological approach, coupled with extensive examples, facilitate not only the understanding of the topic but also the bridging between the various tasks associated with the mining of big (high throughput) biological datasets.” (Irina Ioana Mohorianu, zbMATH, Vol. 1384.92002, 2018)