Bioinformatics

Volume II: Structure, Function, and Applications

Gebonden Engels 2016 2e druk 9781493966110
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

This 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 detailand 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.

Specificaties

ISBN13:9781493966110
Taal:Engels
Bindwijze:gebonden
Uitgever:Springer New York
Druk:2

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Inhoudsopgave

<p>3D Computational Modeling of Proteins Using Sparse Paramagnetic NMR Data.- Inferring Function from Homology.-&nbsp;Inferring Functional Relationships from Conservation of Gene Order.- Structural and Functional Annotation of Long Non-Coding RNAs.-&nbsp;Construction of Functional Gene Networks Using Phylogenetic Profiles.-&nbsp;Inferring Genome-Wide Interaction Networks.-&nbsp;Integrating Heterogeneous Datasets for Cancer Module Identification.-&nbsp;Metabolic Pathway Mining.-&nbsp;Analysis of Genome-Wide Association Data.-&nbsp;Adjusting for Familial Relatedness in the Analysis of GWAS Data.-&nbsp;Analysis of Quantitative Trait Loci.-&nbsp;High-Dimensional Profiling for Computational Diagnosis.-&nbsp;Molecular Similarity Concepts for Informatics Applications.-&nbsp;Compound Data Mining for Drug Discovery.- Studying Antibody Repertoires with Next-Generation Sequencing.-&nbsp;Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures.-&nbsp;Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.-&nbsp;Inference Method for Developing Mathematical Models of Cell Signaling Pathways Using Proteomic Datasets.-&nbsp;Clustering.-&nbsp;Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.-&nbsp;Information Visualization for Biological Data.</p>

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