000			03364cam a22003255i 4500
001			17395163
003			EG-ScBUE
005			20260311100716.0
008			120720t2013 flua f b 001 0 eng d
020			_a9780849328015
040			_aEG-ScBUE _beng _erda _cEG-ScBUE _dEG-ScBUE
082	0	4	_a572.330285 _bDUA _222
100	1		_aDua, Sumeet, _eauthor.
245	1	0	_aData mining for bioinformatics / _cSumeet Dua, Pradeep Chowriappa.
264		1	_aBoca Raton, FL : _bCRC Press / Taylor & Francis Group, _c[2013]
264		4	_cc2013
300			_axix, 328 pages : _billustrations ; _c24 cm
336			_2rdacontent _atext _btxt
337			_aunmediated _2rdamedia _bn
338			_avolume _bnc _2rdacarrier
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
653			_bCOMSCI _cJanuary2017
655			_vReading book
700	1		_aChowriappa, Pradeep, _eauthor.
942			_2ddc _cBB
999			_c23820 _d23792