Catalogue


Computational methods in biomedical research /
edited by Ravindra Khattree and Dayanand Naik.
imprint
Boca Raton : Taylor & Francis, c2008.
description
xvii, 408 p.
ISBN
1584885777, 9781584885771 (alk. paper)
format(s)
Book
Holdings
More Details
imprint
Boca Raton : Taylor & Francis, c2008.
isbn
1584885777
9781584885771 (alk. paper)
general note
"A CRC title."
catalogue key
6363039
 
Includes bibliographical references and index.
A Look Inside
Reviews
This item was reviewed in:
SciTech Book News, June 2008
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Summaries
Main 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 Researchexplores 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.
Back Cover Copy
Using real-world data from various disease areas, this volume covers microarray data analysis, machine learning techniques, mass spectrometry-based protein profiling, state space models, multistate models, Bayesian techniques, the sequential monitoring of randomization tests, mixed-effects models, classification rules for repeated measures data, and estimation methods for analyzing longitudinal data.
Main 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 Researchexplores 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 ofrandomization 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.
Bowker Data Service Summary
This update of recent computational and methodological developments in medical pharmaceutical statistics emphasizes the applicability of techniques and medical and biopharmaceutical applications.
Table of Contents
Preface
Microarray Data Analysis
Machine Learning Techniques for Bioinformatics: Fundamentals and Applications
Machine Learning Methods for Cancer Diagnosis and Prognostication
Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges
Predicting US Cancer Mortality Counts Using State Space Models
Analyzing Multiple Failure Time Data Using SAS
Software
Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data
Bayesian Computational Methods in Biomedical Research
Sequential Monitoring of Randomization Tests
Proportional Hazards Mixed-Effects Models and Applications
Classification Rules for Repeated Measures Data from Biomedical Research
Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research
Index
Table of Contents provided by Publisher. All Rights Reserved.

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