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IDEAL
2004
Springer
13 years 10 months ago
Visualisation of Distributions and Clusters Using ViSOMs on Gene Expression Data
Microarray datasets are often too large to visualise due to the high dimensionality. The self-organising map has been found useful to analyse massive complex datasets. It can be us...
Swapna Sarvesvaran, Hujun Yin
NN
2002
Springer
226views Neural Networks» more  NN 2002»
13 years 4 months ago
Data visualisation and manifold mapping using the ViSOM
The self-organising map (SOM) has been successfully employed as a nonparametric method for dimensionality reduction and data visualisation. However, for visualisation the SOM requ...
Hujun Yin
BMCBI
2006
213views more  BMCBI 2006»
13 years 4 months ago
CoXpress: differential co-expression in gene expression data
Background: Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find...
Michael Watson
BMCBI
2008
142views more  BMCBI 2008»
13 years 4 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
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...
Fang-Xiang Wu
BIODATAMINING
2008
135views more  BIODATAMINING 2008»
13 years 4 months ago
Fast Gene Ontology based clustering for microarray experiments
Background: Analysis of a microarray experiment often results in a list of hundreds of diseaseassociated genes. In order to suggest common biological processes and functions for t...
Kristian Ovaska, Marko Laakso, Sampsa Hautaniemi