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Bioinformatics

Volume II: Structure, Function, and Applications - Previously published in hardcover
BuchKartoniert, Paperback
426 Seiten
Englisch
Springererschienen am06.07.20182. Aufl.
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.mehr
Verfügbare Formate
BuchGebunden
EUR149,79
BuchKartoniert, Paperback
EUR101,64

Produkt

KlappentextThis 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.
Details
ISBN/GTIN978-1-4939-8250-9
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2018
Erscheinungsdatum06.07.2018
Auflage2. Aufl.
ReiheMethods in Molecular Biology
Seiten426 Seiten
SpracheEnglisch
Gewicht832 g
IllustrationenXI, 426 p. 88 illus., 26 illus. in color.
Artikel-Nr.48422084

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr
Kritik
"The book is as an excellent starting point for a wide audience including undergraduates, graduates and established researchers alike. The amount of detail presented for each methodological approach, coupled with extensive examples, facilitate not only the understanding of the topic but also the bridging between the various tasks associated with the mining of big (high throughput) biological datasets." (Irina Ioana Mohorianu, zbMATH, Vol. 1384.92002, 2018)mehr