Due to the large number of genes measured in a typical microarray dataset, feature selection plays an essential role in tumor classification. In turn, relevance and redundancy are ...
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...
Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in m...
This paper discusses different approaches for integrating biological knowledge in gene expression analysis. Indeed we are interested in the fifth step of microarray analysis pro...
Background: Expression microarrays represent a powerful technique for the simultaneous investigation of thousands of genes. The evidence that genes are not randomly distributed in...
Federico E. Turkheimer, Federico Roncaroli, Benoit...