000			03177cam a22002775a 4500
001			17395163
005			20170103151008.0
008			120720t2013 flua frb 001 0 eng d
020			_a9780849328015
020			_a0849328012
040			_aDLC _beng _cDLC _dDLC _dEG-ScBUE
082	0	4	_222 _a572.330285 _bDUA
100	1		_aDua, Sumeet.
245	1	0	_aData mining for bioinformatics / _cSumeet Dua, Pradeep Chowriappa.
260			_aBoca Raton : _bCRC Press / Taylor & Francis Group, _cc.2013.
300			_axix, 328 p. : _bill. ; _c24 cm.
504			_aIncludes bibliographical references and index.
520			_a"Data Mining for Bioinformatics enables researchers to meet the challenge of mining vast amounts of biomolecular data to discover real knowledge. Covering theory, algorithms, and methodologies, as well as data mining technologies, the book presents a thorough discussion of data-intensive computations used in data mining applied to bioinformatics. The book explains data mining design concepts to build applications and systems. It shows how to prepare raw data for the mining process and is filled with heuristics that speed the data mining process"--
520			_a"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"--
650		7	_aBioinformatics. _2BUEsh _95545
650		7	_aData mining. _2BUEsh _927695
651			_2BUEsh
653			_bCOMSCI _cJanuary2017
700	1		_aChowriappa, Pradeep
942			_2ddc
999			_c23820 _d23792