Predictive Modeling of Drug Sensitivity - Softcover

Über die Autorin bzw. den Autor

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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This book 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.

• 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.
• The applications of drug sensitivity prediction methodologies.

• Applies mathematical and computational approaches to biological problems.
• Covers all aspects of drug sensitivity modeling, starting from initial data generation to final experimental validation.
• Includes latest results on drug sensitivity modeling based on the latest research findings.
• Provides information on existing data and software resources for applying the mathematical and computational tools available.