Data Mining Techniques for the Life Sciences

Carugo, Oliviero (Edited by)/ Eisenhaber, Frank (Edited by)

ISBN 10: 1493935704 / ISBN 13: 9781493935703

Editore: Humana Press, 2016

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2nd edition. 565 pages. 10.00x7.00x1.25 inches. In Stock.

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Dati bibliografici

• Titolo: Data Mining Techniques for the Life Sciences
• Editore: Humana Press
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 1493935704
• ISBN 13: 9781493935703
• Rilegatura: Hardcover
• Edizione: seconda edizione
• Condizione: Brand New
• Peso: 1,25 chilogrammi

Descrizione articolo

Riassunto

This volume details several important databases and data mining tools. Data Mining Techniques for the Life Sciences, Second Edition guides readers through archives of macromolecular three-dimensional structures, databases of protein-protein interactions, thermodynamics information on protein and mutant stability, “Kbdock” protein domain structure database, PDB_REDO databank, erroneous sequences, substitution matrices, tools to align RNA sequences, interesting procedures for kinase family/subfamily classifications, new tools to predict protein crystallizability, metabolomics data, drug-target interaction predictions, and a recipe for protein-sequence-based function prediction and its implementation in the latest version of the ANNOTATOR software suite. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Second Edition aims to ensure successful results in the further study of this vital field.

Dalla quarta di copertina

High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome profiling, to single-cell sequencing. Having such diversity of alternatives, there is a demand for information by research scientists without experience in HTS that need to choose the most suitable  methodology or combination of platforms  and to define their experimental designs to achieve their specific objectives. Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing