Computational Methods in Biomedical Research
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Computational Methods in Biomedical Research

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Description

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.show more

Product details

  • Hardback | 432 pages
  • 160.02 x 238.76 x 27.94mm | 748.42g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New.
  • 43 black & white illustrations, 5 colour illustrations, 49 black & white tables
  • 1584885777
  • 9781584885771

About Ravindra Khattree

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

Table of contents

Preface 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 Indexshow more