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

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  • Author : Ranadip Pal
  • Publisher : Academic Press
  • Pages : 354 pages
  • ISBN : 012805431X
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKPredictive Modeling of Drug Sensitivity

Predictive Modeling of Drug Sensitivity

Predictive Modeling of Drug Sensitivity
  • Author : Ranadip Pal
  • Publisher : Academic Press
  • Release : 15 November 2016
GET THIS BOOKPredictive 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

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  • Publisher : Woodhead Publishing
  • Release : 26 September 2016
GET THIS BOOKPredictive Modeling of Pharmaceutical Unit Operations

The use of modeling and simulation tools is rapidly gaining prominence in the pharmaceutical industry covering a wide range of applications. This book focuses on modeling and simulation tools as they pertain to drug product manufacturing processes, although similar principles and tools may apply to many other areas. Modeling tools can improve fundamental process understanding and provide valuable insights into the manufacturing processes, which can result in significant process improvements and cost savings. With FDA mandating the use of Quality

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  • Publisher : Humana
  • Release : 10 December 2019
GET THIS BOOKCancer Bioinformatics

This volume covers a wide variety of state of the art cancer-related methods and tools for data analysis and interpretation. Chapters were designed to attract a broad readership, ranging from active researchers in computational biology and bioinformatics developers, clinical oncologists, and anti-cancer drug developers wishing to rationalize their search for new compounds. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, installation instructions for computational tools discussed, explanations of the input

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  • Author : Ian G. Duncan
  • Publisher : ACTEX Publications
  • Release : 17 April 2021
GET THIS BOOKHealthcare Risk Adjustment and Predictive Modeling

This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of

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  • Author : Max Kuhn,Kjell Johnson
  • Publisher : Springer Science & Business Media
  • Release : 17 May 2013
GET THIS BOOKApplied Predictive Modeling

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text

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  • Publisher : Elsevier
  • Release : 26 July 2010
GET THIS BOOKDrug-like Properties: Concepts, Structure Design and Methods

Of the thousands of novel compounds that a drug discovery project team invents and that bind to the therapeutic target, typically only a fraction of these have sufficient ADME/Tox properties to become a drug product. Understanding ADME/Tox is critical for all drug researchers, owing to its increasing importance in advancing high quality candidates to clinical studies and the processes of drug discovery. If the properties are weak, the candidate will have a high risk of failure or be

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  • Author : Frank E. Harrell
  • Publisher : Springer Science & Business Media
  • Release : 09 March 2013
GET THIS BOOKRegression Modeling Strategies

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of