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

• Published in: Biology (Basel, Switzerland) Vol. 11; no. 7; p. 1024
• Main Authors: Bergamini, Carlo M., Bianchi, Nicoletta, Giaccone, Valerio, Catellani, Paolo, Alberghini, Leonardo, Stella, Alessandra, Biffani, Stefano, Yaddehige, Sachithra Kalhari, Bobbo, Tania, Taccioli, Cristian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 07-07-2022; MDPI
• Subjects: Algorithms; Artificial intelligence; Bacteria; Chargaff’s Second Parity rule; Constipation; Datasets; Dietary supplements; Food selection; Genomes; Genomics; Intestinal microflora; Learning algorithms; Machine Learning; Medical research; Microbiomes; Microorganisms; Neural networks; Probiotics; Public health; Shannon’s Entropy; Symbionts; Transfer RNA; tRNA
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology11071024

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 distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information content in their tRNA sequences compared to non-probiotics. In conclusion, we prove for the first time that Machine Learning methods can discriminate human probiotic from non-probiotic organisms underlining information within tRNA sequences as the most important genomic feature in distinguishing them.