Co-clustering algorithm for the identification of cancer subtypes from gene expression data

[...]applications of machine learning methods become an important area for researchers to explore in order to categorize cancer genes into high and low risk groups or subtypes. [...]the gene expression data matrix that has been produced from this technology under various conditions, where the row represent gene and column represents sample [3, 4]. Furthermore, for the gene expression analysis various co-clustering techniques have been implemented [26]. Since Cheng and Church [18] are the discoverers of bi-clustering solution for the NP-hard problem for clustering of gene expression data. Significant genes selection from this high-dimensional dataset is important for clustering. [...]gene network interactions with gene expression data utilized to obtain genes that play vital roles among samples.