Predictive Modeling Of Drug Sensitivity

Predictive Modeling of Drug Sensitivity

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. - 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 the latest results on drug sensitivity modeling that is based on updated research findings - Provides information on existing data and software resources for applying the mathematical and computational tools available

Biomarkers in Drug Discovery and Development

This book continues the legacy of a well-established reference within the pharmaceutical industry – providing perspective, covering recent developments in technologies that have enabled the expanded use of biomarkers, and discussing biomarker characterization and validation and applications throughout drug discovery and development. Explains where proper use of biomarkers can substantively impact drug development timelines and costs, enable selection of better compounds and reduce late stage attrition, and facilitate personalized medicine Helps readers get a better understanding of biomarkers and how to use them, for example which are accepted by regulators and which still non-validated and exploratory Updates developments in genomic sequencing, and application of large data sets into pre-clinical and clinical testing; and adds new material on data mining, economics, and decision making, personal genetic tools, and wearable monitoring Includes case studies of biomarkers that have helped and hindered decision making Reviews of the first edition: "If you are interested in biomarkers, and it is difficult to imagine anyone reading this who wouldn't be, then this book is for you." (ISSX) and "...provides a good introduction for those new to the area, and yet it can also serve as a detailed reference manual for those practically involved in biomarker implementation." (ChemMedChem)

Artificial Intelligence for Translational Pharmacology