Bioinformatics: Volume II: Structure, Function, and Applications (2nd Edition)

Description

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 detail and 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

1. Preface…
2. Table of Contents…
3. Contributing Authors…
4. Part I Structure, Function, Pathways and Networks
   1. 3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data – Kala Bharath Pilla, Gottfried Otting, and Thomas Huber
   2. Inferring Function from Homology – Tom C. Giles and Richard D. Emes
   3. Inferring Functional Relationships from Conservation of Gene Order – Gabriel Moreno-Hagelsieb
   4. Structural and Functional Annotation of Long Non-Coding RNAs – Martin A. Smith and John S. Mattick
   5. Construction of Functional Gene Networks Using Phylogenetic Profiles – Junha Shin and Insuk Lee
   6. Inferring Genome-Wide Interaction Networks – Goekmen Altay and Onur Mendi
   7. Integrating Heterogeneous Datasets for Cancer Module Identification – A.K.M. Azad
   8. Metabolic Pathway Mining – Jan M. Czarnecki and Adrian J. Shepherd
5. Part II Applications
   1. Analysis of Genome-Wide Association Data – Allan F. McRae
   2. Adjusting for Familial Relatedness in the Analysis of GWAS Data – Russell Thomson and Rebekah McWhirter
   3. Analysis of Quantitative Trait Loci – David L. Duffy
   4. High-Dimensional Profiling for Computational Diagnosis – Claudio Lottaz, Wolfram Gronwald, Rainer Spang, and Julia C. Engelmann
   5. Molecular Similarity Concepts for Informatics Applications – Jurgen Bajorath
   6. Compound Data Mining for Drug Discovery – Jurgen Bajorath
   7. Studying Antibody Repertoires with Next-Generation Sequencing – William D. Lees and Adrian J. Shepherd
   8. Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures – Chloe Warren, Mario Inostroza-Ponta, and Pablo Moscato
   9. Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques – Luke Mathieson, Alexandre Mendes, John Marsden, Jeffrey Pond, and Pablo Moscato
6. Part III Computational Methods
   1. Inference Method for Developing Mathematical Models of Cell Signaling Pathways Using Proteomic Datasets – Tianhai Tian and Jiangning Song
   2. Clustering – Geoffrey J. McLachlan, Richard W. Bean and Shu-Kay Ng
   3. Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems – Falk Huffner, Christian Komusiewicz, Rolf Niedermeier, and Sebastian Wernicke
   4. Information Visualization for Biological Data – Tobias Czauderna and Falk Schreiber

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)

Editor Biography

Jonathan M. Keith
Monash University, Melbourne, Australia