Author: Zheng Zhang; Zena Cai; Zhiying Tan; Congyu Lu; Gaihua Zhang; Yousong Peng
Title: Identification of viruses with the potential to infect human Document date: 2019_4_5
ID: lnch3qsq_8
Snippet: Because the number of human-infecting viruses was much smaller than that of other viruses, the functions of "BalanceBaggingClassifier" (Breiman 1996 , Barandiaran 1998 ) and "BalanceRandomForest" (Chen, Liaw et al. 2004) in the package of "imbalanced-learn" (version 0.4.3) in Python were used to deal with the imbalance . CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which.....
Document: Because the number of human-infecting viruses was much smaller than that of other viruses, the functions of "BalanceBaggingClassifier" (Breiman 1996 , Barandiaran 1998 ) and "BalanceRandomForest" (Chen, Liaw et al. 2004) in the package of "imbalanced-learn" (version 0.4.3) in Python were used to deal with the imbalance . CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/597963 doi: bioRxiv preprint between the human-infecting virus and other viruses in the modeling, with the parameter of "n_estimators" set to be 100. Ten-fold cross-validations were used to evaluate the predictive performances of the machine learning models, and were conducted using the functions of 'StratifiedKFold' in the package "scikit-learn" in Python. To measure the predictive performances of the machine learning models, the area under the receiver operating characteristics (ROC) curve (AUC), the accuracy, recall rate, specificity and predictive precision were calculated for each model.
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