Semi-supervised consensus clustering for gene expression data analysis.

ABSTRACT:

Background

Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and domain knowledge.

Methods

We proposed semi-supervised consensus clustering (SSCC) to integrate the consensus clustering with semi-supervised clustering for analyzing gene expression data. We investigated the roles of consensus clustering and prior knowledge in improving the quality of clus

SUBMITTER: Wang Y 

PROVIDER: S-EPMC4036113 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature