Prediction of essential genes using single nucleotide compositional features in genomes of bacteria: a machine learning-based analysis
by Annushree Kurmi; Piyali Sen; Madhusmita Dash; Aswini Kumar Patra; Suvendra Kumar Ray; Siddhartha Sankar Satapathy
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 19, No. 1, 2023

Abstract: Essential genes are crucial for understanding the cellular processes of an organism. In this article, we have done an extensive machine learning-based analysis of single nucleotide composition in 35 bacterial genomes across several phylogenetic groups. With an objective of classifying essential genes from the remaining genes, we have used seven machine learning-based classifiers - logistic regression, Gaussian Naïve Bayes, k-nearest neighbours, decision tree, random forest, extreme gradient boosting and support vector machine. Random forest classifier was a better performer among the seven classifiers and achieved an AUC score of at least 70% for thirteen organisms. Higher AUC scores were achieved for several organisms such as Salmonella enterica, Sphingomonas wittichii, Bacillus thuringiensis, and Streptococcus pyogenes. Prediction result obtained in general from the machine learning-based analysis suggests that the single nucleotide compositional features may be useful in predicting gene essentiality in some bacteria species though not universally.

Online publication date: Mon, 05-Jun-2023

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