Distributed Robust Biclustering Algorithm for Gene Expression Analysis

Show by Cheng and Church to be an NP-complex problem, biclustering algorithms are more complex than the classical one dimensional clustering technique, particularly requiring multiple computing platforms for large and distributed datasets. In this study, we proposed and extension of the robust biclustering algorithm (RoBA) that is capable of performing biclustering on extremely large or geographically distributed set of gene expression data. The distributed version will divide the cluster tasks among A´ processors with negligible communication costs thus making it scalable over large number of computing nodes. The proposed algorithm has been implemented using Matlab MPI and the performance results are reported based on executions on a 1, 2, 3, 4, and 5 nodes Windows PC cluster connected over 100 Mbits links. The experimental results show increased performance with the increased number of nodes on the same set of data.