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008			150903s2005 xxk| o |||| 0|eng d
020			_a9781846281198 _99781846281198
024	7		_a10.1007/b138794 _2doi
035			_avtls000343663
039		9	_a201509031102 _bVLOAD _c201405070514 _dVLOAD _y201402061201 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aHusmeier, Dirk. _eeditor. _9323149
245	1	0	_aProbabilistic Modeling in Bioinformatics and Medical Informatics / _cedited by Dirk Husmeier, Richard Dybowski, Stephen Roberts.
264		1	_aLondon : _bSpringer London, _c2005.
300			_axx, 504 páginas 218 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aAdvanced Information and Knowledge Processing
500			_aSpringer eBooks
505	0		_aProbabilistic Modeling -- A Leisurely Look at Statistical Inference -- to Learning Bayesian Networks from Data -- A Casual View of Multi-Layer Perceptrons as Probability Models -- Bioinformatics -- to Statistical Phylogenetics -- Detecting Recombination in DNA Sequence Alignments -- RNA-Based Phylogenetic Methods -- Statistical Methods in Microarray Gene Expression Data Analysis -- Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks -- Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models -- Medical Informatics -- An Anthology of Probabilistic Models for Medical Informatics -- Bayesian Analysis of Population Pharmacokinetic/Pharmacodynamic Models -- Assessing the Effectiveness of Bayesian Feature Selection -- Bayes Consistent Classification of EEG Data by Approximate Marginalization -- Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis -- A Probabilistic Network for Fusion of Data and Knowledge in Clinical Microbiology -- Software for Probability Models in Medical Informatics.
520			_aProbabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aDybowski, Richard. _eeditor. _9323150
700	1		_aRoberts, Stephen. _eeditor. _9323151
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781852337780
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b138794 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c291724 _d291724