Biological Data Mining  book cover
1st Edition

Biological Data Mining

ISBN 9781138116580
Published June 7, 2017 by Chapman & Hall
736 Pages 169 B/W Illustrations

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Book Description

Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics.

The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications.

This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.

Table of Contents


Consensus Structure Prediction for RNA Alignments

Junilda Spirollari and Jason T.L. Wang

Invariant Geometric Properties of Secondary Structure Elements in Proteins

Matteo Comin, Concettina Guerra, and Giuseppe Zanotti

Discovering 3D Motifs in RNA

Alberto Apostolico, Giovanni Ciriello, Christine E. Heitsch, and Concettina Guerra

Protein Structure Classification Using Machine Learning Methods

Yazhene Krishnaraj and Chandan Reddy

Protein Surface Representation and Comparison: New Approaches in Structural Proteomics

Lee Sael and Daisuke Kihara

Advanced Graph Mining Methods for Protein Analysis

Yi-Ping Phoebe Chen, Jia Rong, and Gang Li

Predicting Local Structure and Function of Proteins

Huzefa Rangwala and George Karypis


Computational Approaches for Genome Assembly Validation

Jeong-Hyeon Choi, Haixu Tang, Sun Kim, and Mihai Pop

Mining Patterns of Epistasis in Human Genetics

Jason H. Moore

Discovery of Regulatory Mechanisms from Gene Expression Variation by eQTL Analysis

Yang Huang, Jie Zheng, and Teresa M. Przytycka

Statistical Approaches to Gene Expression Microarray Data Preprocessing

Megan Kong, Elizabeth McClellan, Richard H. Scheuermann, and Monnie McGee

Application of Feature Selection and Classification to Computational Molecular Biology

Paola Bertolazzi, Giovanni Felici, and Giuseppe Lancia

Statistical Indices for Computational and Data-Driven Class Discovery in Microarray Data

Raffaele Giancarlo, Davide Scaturro, and Filippo Utro

Computational Approaches to Peptide Retention Time Prediction for Proteomics

Xiang Zhang, Cheolhwan Oh, Catherine P. Riley, Hyeyoung Cho, and Charles Buck


Inferring Protein Functional Linkage Based on Sequence Information and Beyond

Li Liao

Computational Methods for Unraveling Transcriptional Regulatory Networks in Prokaryotes

Dongsheng Che and Guojun Li

Computational Methods for Analyzing and Modeling Biological Networks

Nataša Pržulj and Tijana Milenković

Statistical Analysis of Biomolecular Networks

Jing-Dong J. Han and Chris J. Needham


Beyond Information Retrieval: Literature Mining for Biomedical Knowledge Discovery

Javed Mostafa, Kazuhiro Seki, and Weimao Ke

Mining Biological Interactions from Biomedical Texts for Efficient Query Answering

Muhammad Abulaish, Lipika Dey, and Jahiruddin

Ontology-Based Knowledge Representation of Experiment Metadata in Biological Data Mining

Richard H. Scheuermann, Megan Kong, Carl Dahlke, Jennifer Cai, Jamie Lee, Yu Qian, Burke Squires, Patrick Dunn, Jeff Wiser, Herb Hagler, Barry Smith, and David Karp

Redescription Mining and Applications in Bioinformatics

Naren Ramakrishnan and Mohammed J. Zaki


Data Mining Tools and Techniques for Identification of Biomarkers for Cancer

Mick Correll, Simon Beaulah, Robin Munro, Jonathan Sheldon, Yike Guo, and Hai Hu

Cancer Biomarker Prioritization: Assessing the in vivo Impact of in vitro Models by in silico Mining of Microarray Database, Literature, and Gene Annotation

Chia-Ju Lee, Zan Huang, Hongmei Jiang, John Crispino, and Simon Lin

Biomarker Discovery by Mining Glycomic and Lipidomic Data

Haixu Tang, Mehmet Dalkilic, and Yehia Mechref

Data Mining Chemical Structures and Biological Data

Glenn J. Myatt and Paul E. Blower

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Jake Y. Chen is an assistant professor of informatics at Indiana University, an assistant professor of computer science at Purdue University, and director of the Indiana Center for Systems Biology and Personalized Medicine.

Stefano Lonardi is an associate professor of computer science and engineering at the University of California, Riverside.


The book will be useful to those interested in applying data mining to biology. Specialists in interdisciplinary areas will also find the book helpful. Despite the diversity of the topics presented, the editors manage to maintain homogeneity throughout the book. I recommend this book as a valuable resource on biological data mining. The chapters offer a wealth of useful information …
Computing Reviews, January 2011

… Chen and Lonardi present in this book a showcase of successful recent projects in the research area where biology, computer science, and statistics intersect. The editors have done a good job of pulling together the work of over 80 authors into a well-typeset product with high-resolution graphics and even several diagrams of proteins. … The authors leave no stone unturned in terms of topics and techniques. … There is a veritable alphabet soup of special software employed … there is something for everyone with an interest in bioinformatics in this book. Make sure your library has a copy, or that you buy one for yourselves.
International Statistical Review (2010), 78, 3