000			03946nam a22003615i 4500
001			233167
003			ES-VaUE
005			20221220020442.0
007			cr nn 008mamaa
008			180720s2018 xxu| s |||| 0|eng d
020			_a9781493985616
024	7		_a10.1007/978-1-4939-8561-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aData Mining for Systems Biology _bMethods and Protocols _cedited by Hiroshi Mamitsuka.
250			_a2nd edition 2018
264		1	_aNew York, NY _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XI, 243 páginas) _b95 ilustraciones, 86 ilustraciones a color
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 _v1807
505	0		_aIdentifying Bacterial Strains from Sequencing Data -- MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification -- Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas -- Generative Models for Quantification of DNA Modifications -- DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data -- Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language -- Multiple Testing Tool to Detect Combinatorial Effects in Biology -- SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining -- Computing and Visualizing Gene Function Similarity and Coherence with NaviGO -- Analyzing Glycan Binding Profiles Using Weighted Multiple Alignment of Trees -- Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis -- Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing -- Sparse Modeling to Analyze Drug-Target Interaction Networks -- DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank -- MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing -- Disease Gene Classification with Metagraph Representations -- Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG.
520			_aThis fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.
700	1		_aMamitsuka, Hiroshi _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9781493985609
776	0	8	_iPrinted edition: _z9781493985623
776	0	8	_iPrinted edition: _z9781493993260
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-4939-8561-6 _z(usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Protocols_2018
999			_c233167 _d233167