736 Pages 169 B/W Illustrations
    by Chapman & Hall

    734 Pages 169 B/W Illustrations
    by Chapman & Hall

    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.


    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


    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