|Table of Contents|

Research on Soybean Pre-Micro RNA Prediction Model Based on Recursive Feature Elimination and Random Forest Fusion Algorithm(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2020年03期
Page:
401-405
Research Field:
Publishing date:

Info

Title:
Research on Soybean Pre-Micro RNA Prediction Model Based on Recursive Feature Elimination and Random Forest Fusion Algorithm
Author(s):
AN Yu;  CHEN Gui-fen;  LI Jing
(College of Information Technology, Jilin Agricultural University, Changchun 130118, China)
Keywords:
Soybean;  Pre-MicroRNA;  Recursive Feature Elimination(RFE);  Random Forest(RF);  Prediction model
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2020.03.0401
Abstract:
With the continuous in-depth research on the biological regulatory effects of small genes in soybean, the use of data mining technology to effectively predict the pre-MicroRNA of soybean has become an important development direction in this field. To solve the problem that conventional Random Forest (RF) algorithm has low recognition accuracy in pre-MicroRNA prediction model, this study proposed and constructed a soybean pre-microRNA prediction model based on Recursive Feature Elimination (RFE) and RF fusion algorithm. Firstly, we used the RFE method to select the optimal feature subset of soybean pre-MicroRNA sequences. Then, we constructed a prediction model of soybean pre-MicroRNA based on RF algorithm. Finally, we compared the prediction results of the RFE-RF fusion model with the prediction results of the single RF and Support Vector Machine(SVM) classification model. The results showed that the accuracy of the soybean Pre-MicroRNA prediction model constructed after fusion was significantly improved, reaching 84.62%, 17.02% higher than the model constructed by SVM algorithm, and 14.58% higher than the model constructed by RF algorithm alone. This method provides a new idea for the prediction of pre-MicroRNA genes in soybean.

References:

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Last Update: 2020-07-14