Revealing potential drug-disease-gene association patterns for precision medicine

Fuzzy and Rough Set Theory Based Computational Framework for Mining Genetic Interaction Triplets From Gene Expression Profiles for Lung Adenocarcinoma

Genetic interactions are very helpful in understanding different disease and discovering drugs for it. Compared to the gene pairs that represent the genetic interactions between two genes, the gene triplets are more informative and useful. However, existing works on genetic interactions among gene triplets have primarily focused on detecting gene triplets from time series gene expression profiles. Generating the time series gene expression profiles for humans is quite impracticable but the labeled gene expression profiles are available for different diseases in case of humans. In this paper, a computational framework has been proposed to detect gene triplets from labeled gene expression profiles. First, it employs Rough Set Theory for extracting the key genes and then designs a fuzzy inference system for generating possible gene triplets. Further, Root Mean Squared Error measure has been used to prune out the irrelevant gene triplets. In the present work, the proposed computational framework has been applied to labeled lung adenocarcinoma dataset and can be applied to any other labeled gene expression dataset. The extracted gene triplets and their functionalities have been verified with existing biological literature and benchmark databases and the results of verification signify that the proposed framework is promising in terms of finding useful genetic triplets. Further, the proposed framework has been found more efficient as compared to an existing mutual information-based technique in terms of detecting known genetic interactions.