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. 2018 May 3;9:872. doi: 10.3389/fmicb.2018.00872

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Copyright © 2018 Wang, Fu, Ren, Yu, Chen and Sun.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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(A) The distribution of ASS values of the 37,302 single-logical-feature predictors and 345 single-numerical logistic-regression predictors on the identified group-specific features for training, validation, and testing sets. These predictors achieved better performance in the validation set compared to the training set. A total of 35,652 group-specific features achieved ASS ≥ 0.8 for the validation set, and 12,944 of them achieved ASS ≥ 0.8 for the testing set. (B) ROC curves of the random forests classifier with the top 10 features on validation and testing sets. Using the top 10 group-specific 40-mers, the random forests classifier achieved AUC of 0.963, 0.969, and 0.942 on training, validation, and testing sets, respectively.