Data Mining Techniques for the Life Sciences / edited by Oliviero Carugo, Frank Eisenhaber.

Format:

Book

Contributor:

Carugo, Oliviero, Editor.

Eisenhaber, Frank, Editor.

SpringerLink (Online service)

Series:

Springer Protocols (Springer-12345)

Methods in molecular biology 1940-6029 ; 2449

Methods in Molecular Biology, 1940-6029 ; 2449

Language:

English

Subjects (All):

Bioinformatics.

Local Subjects:

Bioinformatics.

Physical Description:

1 online resource (XIII, 390 pages) : 88 illustrations, 77 illustrations in color.

Edition:

3rd ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

New York, NY : Springer US : Imprint: Humana, 2022.

System Details:

text file PDF

Summary:

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 further their study in this field.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-1-0716-2095-3

9781071620953

Access Restriction:

Restricted for use by site license.