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By: (Edited by) Jake Y. Chen , (Edited by) Jake Y. Chen , (Edited by) Stefano Lonardi , (Edited by) Stefano Lonardi
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This volume presents comprehensive data mining concepts, theories, and applications in current biological and medical research. 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 data mining opportunities for biomedical applications.
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.
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