Biological Data Mining

Jake Y. (Indiana University-Purdue University Chen

ISBN 10: 1420086847 / ISBN 13: 9781420086843

Published by Taylor & Francis Ltd Sep 2009, 2009

About this Item

Neuware - With techniques spanning the entire spectrum of bioinformatics, this book features the latest data mining and knowledge discovery advances for post-genome biology. It includes computational biology methods, algorithms, and software that can help sequence analysis, protein structure analysis, comparative genomics, functional genomics, statistical genetics, proteomics, network analysis, integrative systems biology, and translational biomedical applications. Containing contributions from leading experts, the book also shows how to use combinations of computational techniques in data mining or related data management and information sciences to solve real biological problems.

Seller Inventory # 9781420086843

Bibliographic Details

• Title: Biological Data Mining
• Publisher: Taylor & Francis Ltd Sep 2009
• Publication Date: 2009
• Language: English
• ISBN 10: 1420086847
• ISBN 13: 9781420086843
• Binding: Buch
• Condition: Neu
• Dimensions: 244W x 166H x 42D millimeters 244 millimeters width by 166 millimeters height by 42 millimeters depth
• Weight: 1158 grams

About this title

Synopsis

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.

About the Author

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.