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.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Für Beruf und Forschung
Computer scientists, engineers, computational biologists, and mathematicians
Maße
Höhe: 235 mm
Breite: 191 mm
Gewicht
ISBN-13
978-0-12-805274-7 (9780128052747)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
Autor*in
Texas Tech University, USA
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
