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Predictive Modeling of Drug Sensitivity
Presents state-of-the-art predictive modeling methods for drug sensitivity that allows users to apply mathematical tools in different biological scenarios
Ranadip Pal (Author)
9780128052747, Elsevier Science
Paperback, published 17 November 2016
354 pages
23.4 x 19 x 2.3 cm, 0.63 kg
Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research challenges. It covers the major mathematical techniques used for modeling drug sensitivity, and includes the requisite biological knowledge to guide a user to apply the mathematical tools in different biological scenarios. This book is an ideal reference for computer scientists, engineers, computational biologists, and mathematicians who want to understand and apply multiple approaches and methods to drug sensitivity modeling. The reader will learn a broad range of mathematical and computational techniques applied to the modeling of drug sensitivity, biological concepts, and measurement techniques crucial to drug sensitivity modeling, how to design a combination of drugs under different constraints, and the applications of drug sensitivity prediction methodologies.
1: Introduction2: Data characterization3: Feature selection and extraction from heterogeneous genomic characterizations4: Validation methodologies5: Tumor growth models6: Overview of predictive modeling based on genomic characterizations7: Predictive modeling based on random forests8: Predictive modeling based on multivariate random forests9: Predictive modeling based on functional and genomic characterizations10: Inference of dynamic biological networks based on perturbation data11: Combination therapeutics12: Online resources13: Challenges
Subject Areas: Enterprise software [UFL], The environment [RN], Genetics [non-medical PSAK], Medical bioinformatics [MBF]
