000			03626nam a22004935i 4500
001			u374380
003			SIRSI
005			20160812084231.0
007			cr nn 008mamaa
008			100607s2010 gw | s |||| 0|eng d
020			_a9783642129520 _9978-3-642-12952-0
040			_cMX-MeUAM
050		4	_aTA329-348
050		4	_aTA640-643
082	0	4	_a519 _223
100	1		_aBolshoy, Alexander. _eauthor.
245	1	0	_aGenome Clustering _h[recurso electrónico] : _bFrom Linguistic Models to Classification of Genetic Texts / _cby Alexander Bolshoy, Zeev (Vladimir) Volkovich, Valery Kirzhner, Zeev Barzily.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_a206p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v286
505	0		_aBiological Background -- Biological Classification -- Mathematical Models for the Analysis of Natural-Language Documents -- DNA Texts -- N-Gram Spectra of the DNA Text -- Application of Compositional Spectra to DNA Sequences -- Marker-Function Profile-Based Clustering -- Genome as a Bag of Genes – The Whole-Genome Phylogenetics.
520			_aThe study of language texts at the level of formal non-semantic models has a long history. Suffice it to say that the well-known Markov chains were first introduced as one of such models. The representation of biological data as text and, consequently, applications of text-analysis models in the field of comparative genomics are substantially newer; nevertheless the methods are well developed. In this book, we try to juxtapose linguistic and bioinformatics models of text analysis. So, it can be read, in a sense, “in two directions” – the book is written so as to appeal to the bioinformatician, who may be interested in finding techniques that had initially appeared in the natural language analysis, and to computational linguist, who may be surprised to discover familiar methods used in bioinformatics. In the presentation of the material, the authors, nevertheless, give preference their professional field - bioinformatics. Therefore, even a specialist in bioinformatics can find something new himself in this book. For example, this book includes a review of the main data mining models generating the text spectra. The chapters of the book assume neither advanced mathematical skills nor beginner knowledge of molecular biology. Relevant biological concepts are introduced in the beginning of the book. Several computer science issues relevant to the topics of the book are reviewed in the three appendices: clustering, sequence complexity, and DNA curvature modeling.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aEngineering mathematics.
650	1	4	_aEngineering.
650	2	4	_aAppl.Mathematics/Computational Methods of Engineering.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aVolkovich, Zeev (Vladimir). _eauthor.
700	1		_aKirzhner, Valery. _eauthor.
700	1		_aBarzily, Zeev. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642129513
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v286
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-12952-0
596			_a19
942			_cLIBRO_ELEC
999			_c202260 _d202260