Identification of viruses with the potential to infect human

Zheng Zhang; Zena Cai; Zhiying Tan; Congyu Lu; Gaihua Zhang; Yousong Peng

To discriminating the human-infecting virus from other viruses, several machine-learning models, including k-nearest neighbor (KNN), random forest (RF), gaussian naive bayes classifier (GNBC), support-vector machine (SVM) and logistic regression (LR), were built based on k-mer frequencies in the viral genome. K-mers of one to six nucleotides were used in the models to evaluating the influence of the k-mer length on the model performance (Table S1 ). Figure 2 showed that the AUC of GNBC, SVM and LR models increased as the increase of the k-mer length from one to six. While for KNN and RF, the AUCs of them peaked at k-mer length of 4 and 3, respectively; then, they began to decrease as the increase of k-mer length. The AUCs of KNN and RF were much larger than those of other models, including GNBC, SVM