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Combining BPSO and ELM Models for Inferring Novel lncRNA-Disease Associations

Author

Listed:
  • Wenqing Yang

    (The Academy of Digital China, Fuzhou University, China)

  • Xianghan Zheng

    (The Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, China)

  • QiongXia Huang

    (The Academy of Digital China, Fuzhou University, China)

  • Yu Liu

    (The Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, China)

  • Yimi Chen

    (The Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, China)

  • ZhiGang Song

    (The Academy of Digital China, Fuzhou University, China)

Abstract

It has been widely known that long non-coding RNA (lncRNA) plays an important role in gene expression and regulation. However, due to a few characteristics of lncRNA (e.g., huge amounts of data, high dimension, lack of noted samples, etc.), identifying key lncRNA closely related to specific disease is nearly impossible. In this paper, the authors propose a computational method to predict key lncRNA closely related to its corresponding disease. The proposed solution implements a BPSO based intelligent algorithm to select possible optimal lncRNA subset, and then uses ML-ELM based deep learning model to evaluate each lncRNA subset. After that, wrapper feature extraction method is used to select lncRNAs, which are closely related to the pathophysiology of disease from massive data. Experimentation on three typical open datasets proves the feasibility and efficiency of our proposed solution. This proposed solution achieves above 93% accuracy, the best ever.

Suggested Citation

  • Wenqing Yang & Xianghan Zheng & QiongXia Huang & Yu Liu & Yimi Chen & ZhiGang Song, 2023. "Combining BPSO and ELM Models for Inferring Novel lncRNA-Disease Associations," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(2), pages 1-18, January.
    • Handle: RePEc:igg:jdwm00:v:19:y:2023:i:2:p:1-18
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