Abstract
MicroRNAs (miRNAs) play a significant role in the occurrence and development of complex diseases. The regulatory level of multiple miRNAs is stronger than that of a single miRNA. Therefore, using miRNA combinations to treat complex diseases has become a promising strategy, which provides great insights for exploring disease-associated miRNA combinations and comprehending the miRNA synergistic mechanism. However, current researches mainly focus on the miRNA-disease binary association, or merely the synergetic miRNAs on specific diseases, which may cause incomplete understanding of the pathogenesis of complex diseases. In this work, we present a novel tensor factorization model, name MAGTF, to predict disease-associated miRNA combinations. MAGTF exploits a graph attention neural network to learn the node features over multi-source similarity networks. Then, a feature aggregation module is applied to capture the heterogeneous features over miRNA-disease association network. The learned features are spliced to reconstruct the association tensor for predicting disease-associated miRNA combinations. Empirical results showed the powerful predictive ability of the proposed model. Ablation study indicated the contribution of each module in MAGTF. Moreover, case studies further demonstrated the effectiveness of MAGTF in identifying potential disease-associated miRNA combinations.
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Change history
01 November 2022
In the original version of this chapter, the last name of Jiawei Luo was misspelled. This was corrected.
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This work has been supported by the Natural Science Foundation of China (Grant no. 61873089) and (Grant no. 62032007).
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You, S., Lai, Z., Luo, J. (2022). Multi-source Data-Based Deep Tensor Factorization for Predicting Disease-Associated miRNA Combinations. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_72
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