UID:
almahu_9949251679002882
Umfang:
1 online resource (XI, 324 p. 63 illus., 54 illus. in color.)
Ausgabe:
3rd ed. 2019.
ISBN:
1-4939-9089-6
Serie:
Methods in Molecular Biology, 1939
Inhalt:
This third edition volume expands on the previous editions with new topics that cover drug discovery through translational bioinformatics, informatics, clinical research informatics, as well as clinical informatics. The chapters discuss new methods to study target identification, genome analysis, cheminformatics, protein analysis, and text mining. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials, software workflows, reagents and on-line resources, together with step-by-step, readily reproducible laboratory and computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Bioinformatics and Drug Discovery, Third Edition is a valuable resource for anyone interested in drug design, including academicians (biologists, informaticists and data scientists, chemists, and biochemists), clinicians, and pharmaceutical scientists.
Anmerkung:
Miniaturized Checkerboard Assays to Measure Antibiotic Interactions -- High-Throughput Screening for Drug Combinations -- Post-Processing of Large Bioactivity Data -- How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM) -- A Guide to Dictionary-Based Text Mining -- Leveraging Big Data to Transform Drug Discovery -- How to Prepare a Compound Collection Prior to Virtual Screening -- Building a Quantitative Structure-Property Relationship (QSPR) Model -- Isomeric and Conformational Analysis of Small Drug and Drug-Like Molecules by Ion Mobility Mass Spectrometry (IM-MS) -- A Computational Platform and Guide for Acceleration of Novel Medicines and Personalized Medicine -- Omics Data Integration and Analysis for Systems Pharmacology -- Bioinformatics Based Tools and Software in Clinical Research: A New Emerging Area -- Text Mining for Drug Discovery -- Big Data Cohort Extraction for Personalized Statin Treatment and Machine Learning -- Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data -- Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data -- A Bayesian Network Approach to Disease Subtype Discovery.
Weitere Ausg.:
ISBN 1-4939-9088-8
Sprache:
Englisch
DOI:
10.1007/978-1-4939-9089-4
