Machine Learning Algorithms Highlight tRNA Information Content and Chargaff's Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics.

ABSTRACT: Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distingu

SUBMITTER: Bergamini CM 

PROVIDER: S-EPMC9311688 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature