A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism.

Journal: BMC cancer
Published Date: Jun 5, 2024

Abstract

BACKGROUND: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn't been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training.

Authors

  • Hou Biyu
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
  • Li Mengshan
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China. msli@gnnu.edu.cn.
  • Hou Yuxin
    College of Computer Science and Engineering, Shanxi Datong University, Datong, Shanxi, 037000, China.
  • Zeng Ming
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
  • Wang Nan
    College of Life Sciences, Jiaying University, Meizhou, Guangdong, 514000, China.
  • Guan Lixin
    College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.

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