基于融合神经网络的LncRNA与疾病关联预测方法

doi:10.11896/jsjkx.221000202

计算机科学 ›› 2023, Vol. 50 ›› Issue (8): 226-232.doi: 10.11896/jsjkx.221000202

基于融合神经网络的LncRNA与疾病关联预测方法

李巧君^1,2, 章文³, 杨伟^1,2

1 河南省工业物联网应用工程技术研究中心河南南阳 473000
2 河南工业职业技术学院电子信息工程学院河南南阳 473000
3 华中农业大学信息学院武汉 430070

收稿日期:2022-10-25 修回日期:2022-12-23 出版日期:2023-08-15 发布日期:2023-08-02
通讯作者: 李巧君(qiaojun209@163.com)
基金资助:
河南省科技攻关项目(212102310086)

Fusion Neural Network-based Method for Predicting LncRNA-disease Association

LI Qiaojun^1,2, ZHANG Wen³, YANG Wei^1,2

1 Henan Province Industrial Internet of Things Application Engineering Technology Research Centre,Nanyang,Henan 473000,China
2 School of Electronic Information Engineering,Henan Polytechnic Institute,Nanyang,Henan 473000,China
3 College of Informatics,Huazhong Agricultural University,Wuhan 430070,China

Received:2022-10-25 Revised:2022-12-23 Online:2023-08-15 Published:2023-08-02
About author:LI Qiaojun,born in 1983,master,associate professor,is a member of China Computer Federation.Her main research interests include neural network and big data mining.
Supported by:
Henan Science and Technology Project(212102310086).

摘要/Abstract

摘要： 长链非编码RNA(Long non-coding RNA,LncRNA)的异常表达与疾病的生理和病理过程密切相关,识别LncRNA与疾病之间的潜在关联有助于理解疾病的分子发病机制。以往的方法未能深度整合异构的多源数据以及学习高维特征表示。为此,文中提出了一种基于融合神经网络(Fusion Neural Networks,FNN)预测候选疾病相关LncRNA的方法FNNLDA。FNNLDA整合与LncRNA、疾病和miRNAs相关的多种数据,采用多模型融合思想,利用栈式自编码器和融合神经网络两种深度学习模型分别学习LncRNA-疾病对的高级特征,最后融合两个模块的预测分值来预测LncRNA-疾病的关联性。五折交叉验证显示FNNLDA方法的AUC值比SIMCLDA,MFLDA,CNNLDA和LRLSLDA分别提升了12.5%,15.1%,3.4%和5.8%,表明其在LncRNA-疾病预测性能上有较大提升。基于胃癌疾病案例进行研究,结果证明FNNLDA能够有效识别与疾病关联的潜在LncRNA。

关键词: LncRNA-疾病, 关联预测, 融合神经网络, 栈氏自编码器

Abstract: Aberrant expression of long non-coding RNA(LncRNA) is closely associated with the physiological and pathological processes of diseases.Identifying potential associations between LncRNA and diseases are helpful to understand the molecular pathogenesis of diseases.Previous researches were scarcely integrated with heterogeneous multi-source data and seldom learned high-dimensional feature representations.In this paper,we propose a new method named FNNLDA,which based on fusion neural networks(FNN) to predict the associated LncRNAs of candidate disease.FNNLDA integrates multiple data related to LncRNAs,diseases,and miRNAs.And employs the idea of multi-model fusion to learn high-level features of LncRNA-disease pairs by using two deep learning models:stacked self-encoder and fusion neural network,separately.Finally,fusing the prediction scores of the two modules to predict the LncRNA-disease associations.Five-fold cross-validation test show that the AUC value of FNNLDA method is 12.5%,15.1%,3.4% and 5.8% higher than that of SIMCLDA,MFLDA,CNNLDA and LRLSLDA,respectively.It indicates that this method has a significant improvement in LncRNA-disease prediction.The results of the study based on stomach cancer disease cases demonstrate that FNNLDA can effectively identify potential LncRNAs associated with disease.

Key words: LncRNA-disease, Association prediction, Fusion neural network, Stacked autoencoder

中图分类号:

TP391

李巧君, 章文, 杨伟. 基于融合神经网络的LncRNA与疾病关联预测方法[J]. 计算机科学, 2023, 50(8): 226-232. https://doi.org/10.11896/jsjkx.221000202

LI Qiaojun, ZHANG Wen, YANG Wei. Fusion Neural Network-based Method for Predicting LncRNA-disease Association[J]. Computer Science, 2023, 50(8): 226-232. https://doi.org/10.11896/jsjkx.221000202

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基于融合神经网络的LncRNA与疾病关联预测方法

Fusion Neural Network-based Method for Predicting LncRNA-disease Association

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