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
Sequence, Structure, and Function. Genomics, Transcriptomics, and Proteomics. Functional and Molecular Interaction Networks. Literature, Ontology, and Knowledge Integration. Genome Medicine Applications.
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