Tunable biclustering algorithm for analyzing large gene expression data sets

Amartya Singh
Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We have proposed a graph-based Tunable Biclustering Algorithm (TuBA) (Chapter 2) based on a novel pairwise proximity measure that leverages the size of the data sets...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.