期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
BDLR:lncRNA identification using ensemble learning
1
作者 LEJUN GONG shehai zhou +3 位作者 JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO 《BIOCELL》 SCIE 2022年第4期951-960,共10页
Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with ... Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human diseases.There are many limitations in identifying and annotating lncRNAs using traditional biological experimental methods.With the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning method.Based on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called BDLR.The identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random Forest.The experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other methods.Moreover,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning. 展开更多
关键词 lncRNAs High-throughput sequencing Ensemble learning BAGGING Decision Tree
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部