Identification of gene expression linked to malignancy of human colorectal carcinoma using restricted boltzmann machines

Learning hidden information or pattern on gene expression data to uncover an underlying molecular features is called gene expression profiling. To perform gene expression profiling, an unsupervised machine learning method can be employed. In this paper, Gaussian RBM is proposed to obtain the optimal number of clusters and their members on human colorectal cancer dataset provided by Muro. Gaussian RBM forms two large numbers of genes clusters and one smaller cluster which has several tumour-classifier genes as its members. The two large numbers of genes clusters formed by Gaussian RBM succeed in showing a significant correlation with the existence of tumour and distant metastasis but they show no significant correlation with lymph node metastasis existence. The smaller number of genes clusters gives a statistically significant result in clustering patients into two groups.