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- DescriptionLike a data-guzzling turbo engine, advanced data mining has been powering post-geme 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 kwledge 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 geme 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.
- Author BiographyJake 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.
- PublisherTaylor & Francis Ltd
- Date of Publication08/09/2009
- SubjectLife Sciences: General
- Series TitleChapman & Hall/CRC Data Mining and Knowledge Discovery Series
- Series Part/Volume Numberv. 11
- Country of PublicationUnited States
- ImprintChapman & Hall/CRC
- Content Note169 black & white illustrations, 69 black & white tables
- Weight1133 g
- Width156 mm
- Height235 mm
- Spine43 mm
- Edited byJake Y. Chen,Stefano Lonardi
- Format DetailsUnsewn / adhesive bound
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