Predictive Modeling of Drug Sensitivity - 1st Edition - ISBN: 9780128052747, 9780128054314

Predictive Modeling of Drug Sensitivity

1st Edition

Authors: Ranadip Pal
eBook ISBN: 9780128054314
Paperback ISBN: 9780128052747
Imprint: Academic Press
Published Date: 17th November 2016
Page Count: 354
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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.

Key Features

  • 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

Readership

Computer scientists, engineers, computational biologists, and mathematicians

Table of Contents

  • Preface
  • Chapter 1: Introduction
    • Abstract
    • 1.1 Cancer Statistics
    • 1.2 Promise of Targeted Therapies
    • 1.3 Market Trends
    • 1.4 Roadblocks to Success
    • 1.5 Overview of Research Directions
  • Chapter 2: Data characterization
    • Abstract
    • 2.1 Introduction
    • 2.2 Review of Molecular Biology
    • 2.3 Genomic Characterizations
    • 2.4 Pharmacology
    • 2.5 Functional Characterizations
  • Chapter 3: Feature selection and extraction from heterogeneous genomic characterizations
    • Abstract
    • 3.1 Introduction
    • 3.2 Data-Driven Feature Selection
    • 3.3 Data-Driven Feature Extraction
    • 3.4 Multiomics Feature Extraction and Selection
  • Chapter 4: Validation methodologies
    • Abstract
    • 4.1 Introduction
    • 4.2 Fitness Measures
    • 4.3 Sample Selection Techniques for Accuracy Estimation
    • 4.4 Small Sample Issues
    • 4.5 Experimental Validation Techniques
  • Chapter 5: Tumor growth models
    • Abstract
    • 5.1 Introduction
    • 5.2 Exponential Linear Models
    • 5.3 Logistic and Gompertz Models
    • 5.4 Power Law Models
    • 5.5 Stochastic Tumor Growth Models
    • 5.6 Modeling Tumor Spheroid Growth
    • 5.7 Discussion
  • Chapter 6: Overview of predictive modeling based on genomic characterizations
    • Abstract
    • 6.1 Introduction
    • 6.2 Predictive Modeling Techniques
    • 6.3 Applications
  • Chapter 7: Predictive modeling based on random forests
    • Abstract
    • 7.1 Introduction
    • 7.2 Random Forest Regression
    • 7.3 Combining Models Trained on Different Genomic Characterizations
    • 7.4 Probabilistic Random Forests
  • Chapter 8: Predictive modeling based on multivariate random forests
    • Abstract
    • 8.1 Introduction
    • 8.2 MRF Based on Covariance Approach
    • 8.3 MRF Based on Copula
  • Chapter 9: Predictive modeling based on functional and genomic characterizations
    • Abstract
    • 9.1 Introduction
    • 9.2 Mathematical Formulation
    • 9.3 Data Preprocessing: Drug Target and Output Sensitivity Normalization
    • 9.4 Model Generation
    • 9.5 Generate Tumor Proliferation Circuit
    • 9.6 Model Refinement
    • 9.7 Prediction Error Analysis
    • 9.8 Application Results
    • 9.9 Discussion
  • Chapter 10: Inference of dynamic biological networks based on perturbation data
    • Abstract
    • 10.1 Introduction
    • 10.2 Discrete Deterministic Dynamic Model Inference
    • 10.3 Discrete Stochastic Dynamic Model Inference
    • 10.4 Discussion
  • Chapter 11: Combination therapeutics
    • Abstract
    • 11.1 Introduction
    • 11.2 Analyzing Drug Combinations
    • 11.3 Model-Based Combination Therapy Design
    • 11.4 Model-Free Combination Therapy Design
  • Chapter 12: Online resources
    • Abstract
    • 12.1 Pathway Databases
    • 12.2 Drug-Protein Interaction and Protein Structure Databases
    • 12.3 Drug Sensitivity, Genetic Characterization, and Functional Databases
    • 12.4 Drug Toxicity
    • 12.5 Missing Value Estimation
    • 12.6 Regression Tools
    • 12.7 Target Inhibition Maps
    • 12.8 Estimating Drug Combination Synergy
    • 12.9 Survival Analysis
    • 12.10 Prediction Challenges
    • 12.11 Regulatory Information
  • Chapter 13: Challenges
    • Abstract
    • 13.1 Impediments to the Design of Predictive Models for Personalized Medicine
    • 13.2 Tumor Heterogeneity
    • 13.3 Data Inconsistencies
    • 13.4 Prediction Accuracy Limitations
    • 13.5 Toxicity of Combination Therapeutics
    • 13.6 Collaborative Constraints
    • 13.7 Ethical Considerations
  • Index

Details

No. of pages:
354
Language:
English
Copyright:
© Academic Press 2017
Published:
Imprint:
Academic Press
eBook ISBN:
9780128054314
Paperback ISBN:
9780128052747

About the Author

Ranadip  Pal

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.

Affiliations and Expertise

Texas Tech University, USA