XGEM: Predicting Essential miRNAs by the Ensembles of Various Sequence-Based Classifiers With XGBoost Algorithm - Min, Hui; Xin, Xiao-Hong; Gao, Chu-Qiao; Wang, Likun; Du, Pu-Feng (Frontiers in Genetics, 2022)
MicroRNAs (miRNAs) play vital roles in gene expression regulations. Identification of essential miRNAs is of fundamental importance in understanding their cellular functions. Experimental methods for identifying essential miRNAs are always costly and time-consuming. Therefore, computational methods are considered as alternative approaches. Currently, only a handful of studies focused on predicting essential miRNAs. In this work, we propose to predict essential miRNAs using XGBoost framework with CART (Classification and Regression Trees) on various types of sequence-based features. We name this method as XGEM (XGBoost for Essential MiRNAs). The prediction performance of XGEM is promising. In the comparison to other state-of-the art methods, XGEM performed the best, indicating its potential in identifying essential miRNAs. All codes and datasets of this study are deposited in a GitHub repository (https://github.com/minhui803/XGEM).