Extracting Conditions Specific Key Genes from Basal-Like Breast Cancer Gene Expression Data using Gene Co-expression Network

摘要

Drug discovery for specific disease is completely based on the drug target genes. The drug target gene plays a vital role in the different stages of the disease. Finding such key genes from the transcriptomic data is a complex task. In this paper, we have proposed a novel method for finding conditions specific key genes from the Basal-Like Breast Cancer (BLBC) data. The approach is divided into the two phases: Finding the significant biclusters and another is finding the key genes from the significant biclusters. For the first phase, `runibic' biclustering algorithm has been used. The second phase is based on the concept of difference matrix, gene correlation matrix, gene co-expression network construction and key gene identification. Experimental results shows proposed approach has extracted the 95% and 85% significant biclusters at the p-value less than 0.05 and 0.01 respectively. For each significant bicluster, we have identified the key genes. Some of the important identified condition specific key genes are SHC4, PTOV1, PCNX4, STAG1, HMGB2, SHC3, MIB1, TMOD4, ASB8, PIK3CA etc. The identified key genes can be used as a biomarker for the BLBC disease.