Computational Methods in Biomedical Research

Computational Methods in Biomedical Research

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Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new developments so that the methodologies can be properly implemented in the biomedical field. Responding to this need, Computational Methods in Biomedical Research explores important current and emerging computational statistical methods that are used in biomedical research. Written by active researchers in the field, this authoritative collection covers a wide range of topics. It introduces each topic at a basic level, before moving on to more advanced discussions of applications. The book begins with microarray data analysis, machine learning techniques, and mass spectrometry-based protein profiling. It then uses state space models to predict US cancer mortality rates and provides an overview of the application of multistate models in analyzing multiple failure times. The book also describes various Bayesian techniques, the sequential monitoring of randomization tests, mixed-effects models, and the classification rules for repeated measures data. The volume concludes with estimation methods for analyzing longitudinal data.

Supplying the knowledge necessary to perform sophisticated statistical analyses, this reference is a must-have for anyone involved in advanced biomedical and pharmaceutical research. It will help in the quest to identify potential new drugs for the treatment of a variety of diseases.
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Product details

  • Hardback | 432 pages
  • 160.02 x 238.76 x 27.94mm | 748.42g
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New.
  • 312 equations; 49 Tables, black and white; 5 Illustrations, color; 43 Illustrations, black and white
  • 1584885777
  • 9781584885771

Table of contents

Microarray Data Analysis
Susmita Datta, Somnath Datta, Rudolph S. Parrish, and Caryn M. Thompson
Machine Learning Techniques for Bioinformatics: Fundamentals and Applications
Jaroslaw Meller and Michael Wagner
Machine Learning Methods for Cancer Diagnosis and Prognostication
Anne-Michelle Noone and Mousumi Banerjee
Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges
Dayanand N. Naik and Michael Wagner
Predicting US Cancer Mortality Counts Using State Space Models
Kaushik Ghosh, Ram C. Tiwari, Eric J. Feuer, Kathleen A. Cronin, and Ahmedin Jemal
Analyzing Multiple Failure Time Data Using SAS (R) Software
Joseph C. Gardiner, Lin Liu, and Zhehui Luo
Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data
Florin Vaida, Pulak Ghosh, and Lin Liu
Bayesian Computational Methods in Biomedical Research
Hedibert F. Lopes, Peter Muller, and Nalini Ravishanker
Sequential Monitoring of Randomization Tests
Yanqiong Zhang and William F. Rosenberger
Proportional Hazards Mixed-Effects Models and Applications
Ronghui Xu and Michael Donohue
Classification Rules for Repeated Measures Data from Biomedical Research
Anuradha Roy and Ravindra Khattree
Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research
N. Rao Chaganty and Deepak Mav
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About Dayanand Naik

Oakland University, Rochester, Michigan, USA Old Dominion University, Norfolk, Virginia, USA
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