Tools and approaches that used ML or DL approaches to analyze TEs.
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下载链接：
https://figshare.com/articles/dataset/Tools_and_approaches_that_used_ML_or_DL_approaches_to_analyze_TEs_/24177250
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资源简介：
TIR-Learner uses neural network, k-nearest neighbors, random forest, and Adaboost for the ensemble method, while ClassifyTE uses k-nearest neighbors, extra trees, random forest, support vector machine, AdaBoost, logistic regression, Gradient Boosting Classifiers and XGBoost Classifier for the stacking method. Abbreviations: RFSB: Random forest selective binary classifier, C: Classification, D: detection, A: annotation, CL: curation of TE libraries, NI: novel insertions, TU: TransposonUltimate.
创建时间：
2023-09-21
