Predictive Modeling of Drug Sensitivity

Description

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. Content: Front Matter,Copyright,PrefaceEntitled to full textChapter 1 - Introduction, Pages 1-13 Chapter 2 - Data characterization, Pages 15-43 Chapter 3 - Feature selection and extraction from heterogeneous genomic characterizations, Pages 45-81 Chapter 4 - Validation methodologies, Pages 83-107 Chapter 5 - Tumor growth models, Pages 109-120 Chapter 6 - Overview of predictive modeling based on genomic characterizations, Pages 121-148 Chapter 7 - Predictive modeling based on random forests, Pages 149-188 Chapter 8 - Predictive modeling based on multivariate random forests, Pages 189-218 Chapter 9 - Predictive modeling based on functional and genomic characterizations, Pages 219-256 Chapter 10 - Inference of dynamic biological networks based on perturbation data, Pages 257-282 Chapter 11 - Combination therapeutics, Pages 283-313 Chapter 12 - Online resources, Pages 315-323 Chapter 13 - Challenges, Pages 325-333 Index, Pages 335-342