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Data Mining Techniques for the Life Sciences by Oliviero Carugo at Abbey's Bookshop,
Data Mining Techniques for the Life Sciences
— —
Oliviero Carugo Frank Eisenhaber

Data Mining Techniques for the Life Sciences

Oliviero Carugo,  Frank Eisenhaber

$461.95   $369.67

Hardback

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English
Springer-Verlag New York Inc.
05 May 2022
Molecular biology; Data mining
Series: Methods in Molecular Biology
This third edition details new and updated methods and protocols on important databases and data mining tools. Chapters guides readers through archives of macromolecular sequences and three-dimensional structures, databases of protein-protein interactions, methods for prediction conformational disorder, mutant thermodynamic stability, aggregation, and drug response. Quality of structural data and their release, soft mechanics applications in biology, and protein flexibility are considered, too, together with pan-genome analyses, rational drug combination screening and Omics Deep Mining. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials, includes step-by-step, readily reproducible protocols.

 

Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Third Edition aims to be a practical guide to researches to help furthertheir study in this field.
Edited by:   ,
Imprint:   Springer-Verlag New York Inc.
Country of Publication:   United States
Edition:   3rd ed. 2022
Volume:   2449
Dimensions:   Height: 254mm,  Width: 178mm, 
Weight:   954g
ISBN:   9781071620946
ISBN 10:   1071620940
Series:   Methods in Molecular Biology
Pages:   390
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
 EBI data resources.- IMEx databases: displaying molecular interactions into a single, standards-compliant dataset.- Protein Three-dimensional Structure Databases.- Predicting protein conformational disorder and disordered binding sites.- Profiles of natural and designed protein-like sequences effectively bridge protein sequence gaps: Implications in distant homology detection.- Turning failures into applications: the problem of protein ΔΔG prediction.- Dissecting the genome for drug response prediction.- Prediction of the effect of pH on the aggregation and conditional folding of intrinsically disordered proteins with SolupHred and DispHred.- Extracting the dynamic motion of proteins using Normal Mode Analysis.- Pre- and Post- Publication Verification for Reproducible Data Mining in Macromolecular Crystallography.- Soft Statistical Mechanics for Biology.- Uses and abuses of the atomic displacement parameters in structural biology.- Optimizing the Parametrization of Homologue Classification in the Pan-Genome Computation for a Bacterial Species: Case Study Streptococcus pyogenes.- Computational pipeline for rational drug combination screening in patient-derived cells.- Deep Mining from Omics Data.

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