Predictive Modeling of Drug Sensitivity

Predictive Modeling of Drug Sensitivity

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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.
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Product details

  • Paperback | 354 pages
  • 191 x 235 x 17.78mm | 750g
  • Academic Press Inc
  • San Diego, United States
  • English
  • black & white illustrations
  • 0128052740
  • 9780128052747

Table of contents

Chapter 1: Introduction

Chapter 2: Data characterization

Chapter 3: Feature selection and extraction from heterogeneous genomic characterizations

Chapter 4: Validation methodologies

Chapter 5: Tumor growth models

Chapter 6: Overview of predictive modeling based on genomic characterizations

Chapter 7: Predictive modeling based on random forests

Chapter 8: Predictive modeling based on multivariate random forests

Chapter 9: Predictive modeling based on functional and genomic characterizations

Chapter 10: Inference of dynamic biological networks based on perturbation data

Chapter 11: Combination therapeutics

Chapter 12: Online resources

Chapter 13: Challenges
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About Ranadip Pal

Ranadip Pal is an associate professor in the Electrical and Computer Engineering Department, at the Texas Tech University, USA. His research areas are stochastic modeling and control, genomic signal processing, and computational biology. He is the author of more than 60 peer-reviewed articles including publications in high impact journals such as Nature Medicine and Cancer Cell. He has contributed extensively to robustness analysis of genetic regulatory networks and predictive modeling of drug sensitivity. His research group was a top performer in NCI supported drug sensitivity prediction challenge.
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