Data mining for bioinformatics / Sumeet Dua, Pradeep Chowriappa.

"An Auerbach book."

Includes bibliographical references and index.

1. Introduction to bioinformatics -- 2. Biological databases and integration -- 3. Knowledge discovery in databases -- 4. Feature selection and extraction strategies in data mining -- 5. Feature interpretation for biological learning -- 6. Clustering techniques in bioinformatics -- 7. Advanced clustering techniques -- 8. Classification techniques in bioinformatics -- 9. Validation and benchmarking.

"PREFACE The flourishing field of bioinformatics has been the catalyst to transform biological research paradigms to extend beyond traditional scientific boundaries. Fueled by technological advancements in data collection, storage and analysis technologies in biological sciences, researchers have begun to increasingly rely on applications of computational knowledge discovery techniques to gain novel biological insight from the data. As we forge into the future of next-generation sequencing technologies, bioinformatics practitioners will continue to design, develop and employ new algorithms, that are efficient, accurate, scalable, reliable and robust to enable knowledge discovery on the projected exponential growth of raw data. To this end, data mining has been and will continue to be vital for analyzing large volumes of heterogeneous, distributed, semi-structured and interrelated data for knowledge discovery. This book is targeted to readers who are interested in the embodiments of data mining techniques, technologies and frameworks, employed for effective storing, analyzing, and extracting knowledge from large databases specifically encountered in a variety of bioinformatics domains, including but not limited to, genomics and proteomics. The book is also designed to give a broad, yet in-depth overview of the application domains of data mining for bioinformatics challenges. The sections of the book are designed to enable readers from both biology and computer science backgrounds gain an enhanced understanding of the cross-disciplinary field. In addition to providing an overview of the area discussed in Section 1, individual chapters of Sections 2, 3 and 4 are dedicated to key concepts of feature extraction, unsupervised learning, and supervised learning techniques"-- Provided by publisher.

Also available as an electronic resource.