Machine-learning models able to predict phage-bacteria interactions
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modified on 2017-09-11, 09:17 Phage-therapy, a promising alternative to antibiotic-resistance, uses phages to infect and kill pathogenic bacteria. It requires finding perfectly
matching phage-bacterium pairs, a time and money-consuming task, currently achieved empirically in laboratory. Our project aims at
improving this task by predicting, in-silico, if a given phage-bacterium pair would interact. Predictions are performed on the base of public
genomic data combined with machine-learning algorithms. With such an approach we have obtained around 90% of predictive power. In
order to improve these results, we will extend our methodology and we will validate it with newly-generated clinically-relevant data.