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基于潜在因子模型和投影的 lncRNA-疾病关联预测。

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

机构信息

College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, People's Republic of China.

出版信息

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

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.

摘要

计算机辅助的 lncRNA-疾病关联研究是研究 lncRNA-疾病发展的重要途径。对现有数据的相关性分析、预测模型的建立、未知 lncRNA-疾病关联的预测,可以使生物实验具有针对性,提高生物实验的准确性。本文提出了一种基于潜在因子模型和投影的 lncRNA-疾病关联预测模型(LFMP)。该方法利用 lncRNA-miRNA 关联数据和 miRNA-疾病关联数据来预测未知的 lncRNA-疾病关联,因此该方法不需要 lncRNA-疾病关联数据。模拟结果表明,在 LOOCV 框架下,LFMP 的 AUC 可以达到 0.8964,优于最新结果。通过对肺癌和结直肠癌的案例研究,LFMP 可以有效地推断出未检测到的 lncRNA-疾病关联。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7195/8497550/25ca2b5e5d89/41598_2021_99493_Fig1_HTML.jpg

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