BMCBI
2007
14 years 12 days ago
2007
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
BMCBI
2007
14 years 12 days ago
2007
Background: Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, a...
BMCBI
2007
14 years 12 days ago
2007
Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is crit...
BMCBI
2008
14 years 12 days ago
2008
Background: This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascade...
BMCBI
2007
14 years 12 days ago
2007
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
BMCBI
2010
14 years 12 days ago
2010
Background: Many research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful inf...
BMCBI
2008
14 years 12 days ago
2008
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
BMCBI
2010
14 years 12 days ago
2010
Background: There is a large amount of gene expression data that exists in the public domain. This data has been generated under a variety of experimental conditions. Unfortunatel...
BMCBI
2010
14 years 12 days ago
2010
Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...
BIBM
2008
IEEE
14 years 14 days ago
2008
IEEE
This paper addresses the protein classification problem, and explores how its accuracy can be improved by using information from time-course gene expression data. The methods are ...