Prediction of non-coding RNAs in Fusobacterium nucleatum-infected mice using machine learning

This study is the first to apply ML to predict ncRNAs in F. nucleatum-infected mice using transcriptomic data. Random forest and AdaBoost showed superior classification performance in identifying lncRNAs and circRNAs associated with the infection. Further studies with larger cohorts and external validation are needed to confirm these findings.