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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 5
1365-1370

miRNA-disease association prediction based on network representation learning method

Geng Xia
Han Kaijian
School of Computer Science & Communication Engineering, Jiangsu University, Zhenjiang Jiangsu 212013, China
DOI: 10.19734/j.issn.1001-3695.2020.07.0176

Abstract

In view of the problem of inadequate use of information, excessive dependence on similarity information of nodes in the network and low prediction accuracy in miRNA-disease association studies, this paper proposed a miRNA-disease association prediction method based on network representation learning(NRLMDA: network representation learning miRNA-disease association). This method constructed a miRNA-lncRNA-disease heterogeneous network by introducing long-chain noncoding RNA(lncRNA), which enriched the biological information of the original network. It used the network representation learning node2vec algorithm in the heterogeneous network proposed above to obtain the node's neighboring sequence with a certain walking strategy, and performed deep learning through the skip-gram model to obtain the low-dimensional feature vectors of the node. Finally, the association rule inference method based on miRNA-miRNA similarity predicted the association between miRNA and disease. This method could mine the topological structure characteristics of the global network without negative samples. NRLMDA's experimental results on leave-one-out cross-validation and five-fold cross-validation as well as case studies are superior to the classical methods.

Foundation Support

国家自然科学基金—青年基金资助项目(61702229)
江苏省六大人才高峰项目(2016-XYDXXJS-086)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.07.0176
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Algorithm Research & Explore
Pages: 1365-1370
Serial Number: 1001-3695(2021)05-015-1365-06

Publish History

[2021-05-05] Printed Article

Cite This Article

耿霞, 韩凯健. 一种基于网络表示学习的miRNA-疾病关联预测方法 [J]. 计算机应用研究, 2021, 38 (5): 1365-1370. (Geng Xia, Han Kaijian. miRNA-disease association prediction based on network representation learning method [J]. Application Research of Computers, 2021, 38 (5): 1365-1370. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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