App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Review of computational models for non-coding RNA-drug association prediction

Li Nia
Ou Weihuaa
Chen Jiamina
Jin Yihangb
Li Yuzea
Hu Jinsonga
a. School of Big Data and Computer Science, b. School of Mathematical Sciences, Guizhou Normal University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2026.05.0166

Abstract

Non-coding RNAs participate in gene regulation, disease progression, and drug response. Identifying their potential associations with drugs can help reveal drug mechanisms and the regulation of drug resistance and sensitivity. Traditional biological experiments are costly, time-consuming, and low-throughput. Computational models can rapidly screen large-scale potential associations through mathematical modeling and algorithmic learning. Therefore, computational models for non-coding RNA-drug association prediction have become an important research direction. First, the review introduces the basic concepts and theoretical foundations. It then groups input representation methods into two categories according to data representation: object attribute-based methods and relation network-based methods. Second, it classifies existing methods into three categories based on modeling ideas and technical evolution: traditional machine learning, matrix factorization, and graph neural networks, and analyzes their characteristics and limitations. Finally, the review summarizes relevant databases and evaluation metrics, and discusses current challenges and future directions, aiming to provide references for related method design.

Foundation Support

国家自然科学基金资助项目(61972098)
贵州省基础研究计划面上项目(黔科合基础MS[2026]409)
2025年度定向支持贵安新区科技重点项目(黔科合成果DXGA[2025]重点002)
贵州省高层次创新型人才项目(GCC[2023]033)
贵州省教育厅自然科学研究项目(QJJ[2024]009)
贵州省科学技术协会"新质黔沿"引领项目(QKX2026-KC-YZ013)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.05.0166
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-22] Accepted Paper

Cite This Article

李旎, 欧卫华, 陈家民, 等. 面向非编码RNA-药物关联预测的计算模型研究综述 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0166. (Li Ni, Ou Weihua, Chen Jiamin, et al. Review of computational models for non-coding RNA-drug association prediction [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0166. )

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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