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008			161128s2017 xxu| o |||| 0|eng d
020			_a9781493966134
024	7		_a10.1007/978-1-4939-6613-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQH324.2 _b2017 EB
245	0	0	_aBioinformatics : _bVolume II: Structure, Function, and Applications _cedited by Jonathan M. Keith
250			_a2nd edition 2017
264		1	_aNew York, NY _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XI, 426 páginas) _b88 ilustraciones, 26 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 _v1526
505	0		_a3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data -- Inferring Function from Homology -- Inferring Functional Relationships from Conservation of Gene Order -- Structural and Functional Annotation of Long Non-Coding RNAs -- Construction of Functional Gene Networks Using Phylogenetic Profiles -- Inferring Genome-Wide Interaction Networks -- Integrating Heterogeneous Datasets for Cancer Module Identification -- Metabolic Pathway Mining -- Analysis of Genome-Wide Association Data -- Adjusting for Familial Relatedness in the Analysis of GWAS Data -- Analysis of Quantitative Trait Loci -- High-Dimensional Profiling for Computational Diagnosis -- Molecular Similarity Concepts for Informatics Applications -- Compound Data Mining for Drug Discovery -- Studying Antibody Repertoires with Next-Generation Sequencing -- Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures -- Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques -- Inference Method for Developing Mathematical Models of Cell Signaling Pathways Using Proteomic Datasets -- Clustering -- Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems -- Information Visualization for Biological Data.
520			_aThis second edition provides updated and expanded chapters covering a broad sampling of useful and current methods in the rapidly developing and expanding field of bioinformatics. Bioinformatics, Volume II: Structure, Function, and Applications, Second Edition is comprised of three sections: Structure, Function, Pathways and Networks; Applications; and Computational Methods. The first section examines methodologies for understanding biological molecules as systems of interacting elements. The Applications section covers numerous applications of bioinformatics, focusing on analysis of genome-wide association data, computational diagnostic, and drug discovery. The final section describes four broadly applicable computational methods that are important to this field. These are: modeling and inference, clustering, parameterized algorithmics, and visualization. As a volume in the highly successful Methods in Molecular Biology series, chapters feature the kind of detail and expert implementation advice to ensure positive results. Comprehensive and practical, Bioinformatics, Volume II: Structure, Function, and Applications is an essential resource for graduate students, early career researchers, and others who are in the process of integrating new bioinformatics methods into their research. .
988			_aSpringer_Protocols_2017
650		7	_2embne _9160489 _aBioinformática _vManuales de laboratorio
776	0	8	_iPrinted edition: _z9781493966110
776	0	8	_iPrinted edition: _z9781493966127
776	0	8	_iPrinted edition: _z9781493982509
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-4939-6613-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b12/2023 _dz _eu _zSI