A Weak Supervision Technique with a Generative Model for Improved Gene Clustering

In the field of computational bioinformatics, for tasks such as medical diagnosis or disease gene classification, acquiring class labels is very much essential and costly. Now a days, labelling biomedical data became one of the crucial bottlenecks for developing supervised machine learning systems, as well as deep learning systems. On the contrary, weak supervision sources that generate diverse, semi-accurate or programmatically-generated labels are comparatively cheaper and less time consuming. In this paper, we have proposed a framework for integrating weak supervision sources with a generative model to estimate probabilistic class labels of the gene expression data. Here as the weak supervision source, a multi-objective optimization (MOO) based clustering technique is utilized. The non-dominated solutions of Pareto optimal front are viewed as the weak supervised labels. These weak supervised labels are fed to a generative model, which finally assigns probabilistic class labels to different genes. In this work, we have shown that our proposed generative model based clustering technique attains better Silhouette score (some quality measure of clustering) for three real-life NCBI gene expression data sets than other stateof-the-art methods, without using any labeled data. Finally, the superiority of the proposed method is validated by using a statistical and a biological significance test.