Buch, Englisch, Band 1526, 426 Seiten, Previously published in hardcover, Format (B × H): 178 mm x 254 mm, Gewicht: 822 g
Reihe: Methods in Molecular Biology
Volume II: Structure, Function, and Applications
Buch, Englisch, Band 1526, 426 Seiten, Previously published in hardcover, Format (B × H): 178 mm x 254 mm, Gewicht: 822 g
Reihe: Methods in Molecular Biology
ISBN: 978-1-4939-8250-9
Verlag: Springer
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
3D 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.
