000			02777nam a22003978i 4500
001			CR9780511811135
003			UkCbUP
005			20200124160300.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101021s2007||||enk o ||1 0|eng|d
020			_a9780511811135 (ebook)
020			_z9780521877510 (hardback)
020			_z9780521706940 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQH447 _b.M35 2007
082	0	4	_a572.860285 _222
100	1		_aMajoros, William H., _eauthor.
245	1	0	_aMethods for computational gene prediction / _cWilliam H. Majoros.
264		1	_aCambridge : _bCambridge University Press, _c2007.
300			_a1 online resource (xvii, 430 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_a1. Introduction -- 2. Mathematical preliminaries -- 3. Overview of gene prediction -- 4. Gene finder evaluation -- 5. A toy Exon finder -- 6. Hidden Markov models -- 7. Signal and content sensors -- 8. Generalized hidden Markov models -- 9. Comparative gene finding -- 10. Machine Learning methods -- 11. Tips and tricks -- 12. Advanced topics.
520			_aInferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to keep pace with this rapidly-advancing field.
650		0	_aGenomics _xData processing.
650		0	_aBioinformatics.
650		0	_aMolecular genetics _xData processing.
650		0	_aMolecular genetics _xData processing _vCase studies.
650		0	_aMolecular genetics _xMathematics.
650		0	_aMolecular genetics _xMathematics _vCase studies.
776	0	8	_iPrint version: _z9780521877510
856	4	0	_uhttps://doi.org/10.1017/CBO9780511811135
999			_c520286 _d520284