000			04033nam a22003735i 4500
001			392893
003			ES-MaUEC
005			20230102122853.0
007			cr nn 008mamaa
008			100715s2010 xxu| s |||| 0|eng d
020			_a9781603272414
024	7		_a10.1007/978-1-60327-241-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aData Mining Techniques for the Life Sciences _cedited by Oliviero Carugo, Frank Eisenhaber.
250			_a1st edition 2010
264		1	_aTotowa, NJ _bHumana Press _c2010
300			_a1 recurso en línea (XII, 408 páginas) _b89 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v609
505	0		_aDatabases -- Nucleic Acid Sequence and Structure Databases -- Genomic Databases and Resources at the National Center for Biotechnology Information -- Protein Sequence Databases -- Protein Structure Databases -- Protein Domain Architectures -- Thermodynamic Database for Proteins: Features and Applications -- Enzyme Databases -- Biomolecular Pathway Databases -- Databases of Protein-Protein Interactions and Complexes -- Data Mining Techniques -- Proximity Measures for Cluster Analysis -- Clustering Criteria and Algorithms -- Neural Networks -- A User's Guide to Support Vector Machines -- Hidden Markov Models in Biology -- Database Annotations and Predictions -- Integrated Tools for Biomolecular Sequence-Based Function Prediction as Exemplified by the ANNOTATOR Software Environment -- Computational Methods for Ab Initio and Comparative Gene Finding -- Sequence and Structure Analysis of Noncoding RNAs -- Conformational Disorder -- Protein Secondary Structure Prediction -- Analysis and Prediction of Protein Quaternary Structure -- Prediction of Posttranslational Modification of Proteins from Their Amino Acid Sequence -- Protein Crystallizability.
520			_aWhereas getting exact data about living systems and sophisticated experimental procedures have primarily absorbed the minds of researchers previously, the development of high-throughput technologies has caused the weight to increasingly shift to the problem of interpreting accumulated data in terms of biological function and biomolecular mechanisms. In Data Mining Techniques for the Life Sciences, experts in the field contribute valuable information about the sources of information and the techniques used for "mining" new insights out of databases. Beginning with a section covering the concepts and structures of important groups of databases for biomolecular mechanism research, the book then continues with sections on formal methods for analyzing biomolecular data and reviews of concepts for analyzing biomolecular sequence data in context with other experimental results that can be mapped onto genomes. As a volume of the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Authoritative and easy to reference, Data Mining Techniques for the Life Sciences seeks to aid students and researchers in the life sciences who wish to get a condensed introduction into the vital world of biological databases and their many applications.
700	1		_aCarugo, Oliviero _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aEisenhaber, Frank _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9781607614869
776	0	8	_iPrinted edition: _z9781603272407
776	0	8	_iPrinted edition: _z9781493956883
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-60327-241-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Protocols_2010
999			_c392893 _d392893