Disease genes prediction by HMM based PU-learning using gene expression profiles

In this study, a novel method is introduced to predict disease candidate genes through gene expression profiles by learning hidden Markov models. In order to evaluate the proposed method, it is applied on a mixed part of 398 disease genes from three disease types and 12001 unlabeled genes. Compared to the other methods in literature, the experimental results indicate a significant improvement in favor of the proposed method. Graphical abstract
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research