| 000 | 04060nam a22003615i 4500 | ||
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| 001 | 235284 | ||
| 003 | ES-VaUE | ||
| 005 | 20221220020655.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 161128s2017 xxu| s |||| 0|eng d | ||
| 020 | _a9781493966134 | ||
| 024 | 7 |
_a10.1007/978-1-4939-6613-4 _2doi |
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| 040 |
_aES-MaUEC _bspa _cES-MaUEC |
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| 245 | 1 | 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 |
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| 300 |
_a1 recurso en línea (XI, 426 páginas) _b88 ilustraciones, 26 ilustraciones a color |
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| 336 |
_atexto _btxt _2rdacontent |
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| 337 |
_aelectrónico _bc _2rdamedia |
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| 338 |
_arecurso electrónico _bcr _2rdacarrier |
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| 347 |
_aarchivo de texto _bPDF |
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| 490 | 0 |
_aMethods in Molecular Biology _x1940-6029 _v1526 |
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| 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. . | ||
| 700 | 1 |
_aKeith, Jonathan M _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 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 _z(usuarios Universidad Europea de Valencia) |
| 942 |
_2lcc _cLE |
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| 988 | _aSpringer_Protocols_2017 | ||
| 999 |
_c235284 _d235284 |
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