ABSTRACT

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 computatio

chapter 1|44 pages

Microarray Data Analysis

BySusmita Datta, Somnath Datta, Rudolph S. Parrish, Caryn M. Thompson

chapter 2|32 pages

Machine Learning Techniques for Bioinformatics: Fundamentals and Applications

ByJarosław Meller, Michael Wagner

chapter 3|26 pages

Machine Learning Methods for Cancer Diagnosis and Prognostication

ByAnne-Michelle Noone, Mousumi Banerjee

chapter 4|28 pages

Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges

ByDayanand N. Naik, Michael Wagner

chapter 6|36 pages

Analyzing Multiple Failure Time Data Using SAS® Software

ByJoseph C. Gardiner, Lin Liu, Zhehui Luo

chapter 7|22 pages

Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data

ByFlorin Vaida, Pulak Ghosh, Lin Liu

chapter 9|36 pages

Sequential Monitoring of Randomization Tests

ByYanqiong Zhang, William F. Rosenberger

chapter 10|26 pages

Proportional Hazards Mixed-Effects Models and Applications

ByRonghui Xu, Michael Donohue

chapter 11|48 pages

Classification Rules for Repeated Measures Data from Biomedical Research

ByAnuradha Roy, Ravindra Khattree

chapter 12|30 pages

Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research

ByN. Rao Chaganty, Deepak Mav