Data Mining Techniques for the Life Sciences

ISBN-13: 9781071620977 / Angielski / Miękka / 2023

Opis

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.

Spis treści

Part I: DATABASES

 

1 EBI data resources

Rolf Apweiler and Amonida Zadissa

 

2 IMEx databases: displaying molecular interactions into a single, standards-compliant dataset

Pablo Porras, Sandra Orchard and Luana Licata

 

3 Protein Three-dimensional Structure Databases

Vaishali P. Waman, Christine Orengo, Gerard J. Kleywegt and Arthur M. Lesk

 

Part II:  PREDICTION METHODS

 

4 Predicting protein conformational disorder and disordered binding sites

Ketty Tamburrini, Giulia Pesce, Juliet Nilsson, Frank Gondelaud, Andrey V. Kajava, Jean-Guy Berrin and Sonia Longhi

 

5 Profiles of natural and designed protein-like sequences effectively bridge protein sequence gaps: Implications in distant homology detection

Gayatri Kumar, Narayanaswamy Srinivasa and Sankaran Sandhya

 

6 Turning failures into applications: the problem of protein ΔΔG prediction

Rita Casadio, Castrense Savojardo, Piero Fariselli, Emidio Capriotti and Pier Luigi Martelli

 

7 Dissecting the genome for drug response prediction

Gerardo Pepe, Chiara Carrino, Luca Parca, Manuela Helmer-Citterich

 

8 Prediction of the effect of pH on the aggregation and conditional folding of intrinsically disordered proteins with SolupHred and DispHred

Valentín Iglesias, Carlos Pintado-Grima, Jaime Santos, Marc Fornt and Salvador Ventura

 

9 Extracting the dynamic motion of proteins using Normal Mode Analysis

Jacob A. Bauer and Vladena Bauerová

 

Part III: DATA QUALITY

 

10 Pre- and Post- Publication Verification for Reproducible Data Mining in Macromolecular Crystallography

John R Helliwell

 

11 Soft Statistical Mechanics for Biology

Mariano Bizzarri, Alessandro Giuliani

 

12 Uses and abuses of the atomic displacement parameters in structural biology

Oliviero Carugo

 

13 Optimizing the Parametrization of Homologue Classification in the Pan-Genome Computation for a Bacterial Species: Case Study Streptococcus pyogenes

Erwin Tantoso, Birgit Eisenhaber and Frank Eisenhaber

 

Part VI: BIG DATA

 

14 Computational pipeline for rational drug combination screening in patient-derived cells

Paschalis Athanasiadis, Aleksandr Ianevski, Sigrid Skånland and Tero Aittokallio

 

15 Deep Mining from Omics Data

Abeer Alzubaidi and Jonathan Tepper