Identifying the degree of genetic interactions using Restricted Boltzmann Machine—A study on colorectal cancer

• Published in: IET systems biology Vol. 15; no. 1; pp. 26 - 39
• Main Authors: Saha, Sujay, Bandopadhyay, Saikat, Ghosh, Anupam
• Format: Journal Article
• Language: English
• Published: England John Wiley and Sons Inc 01-02-2021; Wiley
• Subjects: Bayes Theorem; bioinformatics; biology computing; Boltzmann machines; cancer; Colorectal Neoplasms - genetics; diseases; Gene Regulatory Networks - genetics; genetics; Humans; Machine Learning; Original Research Paper; Original Research Papers
• ISSN: 1751-8849; 1751-8857
• DOI: 10.1049/syb2.12009

The phenomenon of two or more genes affecting the expression of each other in various ways in the development of a single character of an organism is known as gene interaction. Gene interaction not only applies to normal human traits but to the diseased samples as well. Thus, an analysis of gene interaction could help us to differentiate between the normal and the diseased samples or between the two/more phases any diseased samples. At the first stage of this work we have used restricted Boltzmann machine model to find such significant interactions present in normal and/or cancer samples of every gene pairs of 20 genes of colorectal cancer data set (GDS4382) along with the weight/degree of those interactions. Later on, we are looking for those interactions present in adenoma and/or carcinoma samples of the same 20 genes of colorectal cancer data set (GDS1777). The weight/degree of those interactions represents how strong/weak an interaction is. At the end we will create a gene regulatory network with the help of those interactions, where the regulatory genes are identified by using Naïve Bayes Classifier. Experimental results are validated biologically by comparing the interactions with NCBI databases.