Machine learning with maximization (support) of separating margin (vector), called support
vector machine (SVM) learning, is a powerful classification tool that has been used for …

This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

Abstract Interferome v2. 0 (http://interferome. its. monash. edu. au/interferome/) is an update
of an earlier version of the Interferome DB published in the 2009 NAR database edition …

Praise for the Second Edition:"… this is a useful, comprehensive compendium of almost
every possible sample size formula. The strong organization and carefully defined formulae …

Powerful specialized software is essential for managing, quantifying, and ultimately deriving
scientific insight from results of a microarray experiment. We have developed a suite of …

One of the main objectives in the analysis of microarray experiments is the identification of
genes that are differentially expressed under two experimental conditions. This task is …

Systems biology aims to develop mathematical models of biological systems by integrating
experimental and theoretical techniques. During the last decade, many systems biological …

Extracting biological information from microarray data requires appropriate statistical
methods. The simplest statistical method for detecting differential expression is the t test …

The expression of microRNA in nonalcoholic steatohepatitis (NASH) and their role in the
genesis of NASH are not known. The aims of this study were to:(1) identify differentially …

Mixture modeling provides an effective approach to the differential expression problem in
microarray data analysis. Methods based on fully parametric mixture models are available …