lncRNA-disease association prediction based on latent factor model and projection

Bo Wang; Chao Zhang; Xiao-Xin Du; Jian-Fei Zhang

doi:10.1038/s41598-021-99493-5

lncRNA-disease association prediction based on latent factor model and projection

Sci Rep. 2021 Oct 7;11(1):19965. doi: 10.1038/s41598-021-99493-5.

Authors

Bo Wang¹, Chao Zhang², Xiao-Xin Du², Jian-Fei Zhang²

Affiliations

¹ College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, People's Republic of China. drbowang@163.com.
² College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, People's Republic of China.

Abstract

Computer aided research of lncRNA-disease association is an important way to study the development of lncRNA-disease. The correlation analysis of existing data, the establishment of prediction model, prediction of unknown lncRNA-disease association, can make the biological experiment targeted, improve the accuracy of biological experiment. In this paper, a lncRNA-disease association prediction model based on latent factor model and projection is proposed (LFMP). This method uses lncRNA-miRNA association data and miRNA-disease association data to predict the unknown lncRNA-disease association, so this method does not need lncRNA-disease association data. The simulation results show that under the LOOCV framework, the AUC of LFMP can reach 0.8964. Better than the latest results. Through the case study of lung and colorectal tumors, LFMP can effectively infer the undetected lncRNA-disease association.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Colorectal Neoplasms / genetics*
Computational Biology / methods
Computer Simulation
Female
Genetic Predisposition to Disease
Humans
Lung Neoplasms / genetics*
Male
RNA, Long Noncoding / genetics*

Substances

RNA, Long Noncoding