Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE

Cao, Buwen; Li, Renfa; Xiao, Sainan; Deng, Shuguang; Zhou, Xiangjun; Zhou, Lang

doi:10.1016/j.isci.2022.105299

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…With the continuous advancement of bioinformatics and the advent of the artificial intelligence era, researchers are increasingly using machine learning and deep learning algorithms to predict miRNA-disease associations [ 13 – 15 ]. It can provide validation guidance for biological experiments, thereby conserving resources and further advancing the field of miRNA and disease association prediction [ 16 – 18 ].It also has the potential to drive further advances in miRNA-disease association prediction. Based on different prediction strategies, existing methods can be categorized into four types: machine learning-based methods, information propagation-based methods, scoring function-based methods, and matrix transformation-based methods [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

Biyu,

Mengshan,

Yuxin

et al. 2024

BMC Cancer

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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. Results The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. Conclusions The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others. Graphical Abstract

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