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Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations

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Abstract

LncRNAs play a part in numerous momentous processes of biology such as disease diagnoses, preventions and treatments. The associations between various diseases and lncRNAs are one of the crucial approaches to learn the role and status of lncRNAs in human diseases. With the researches on lncRNA and diseases, multiple methods based on neural network have been employed to predict these associations. However, the deep and complicated characteristic representations of lncRNA-disease associations were failed to be extracted, and the discriminative contributions of the interactions, correlations, and similarities among miRNAs diseases, and lncRNAs for the correlation predictions were ignored. In this paper, based on the multibiology premise of lncRNAs, miRNAs, and diseases, a dual attention network was proposed to predict the model of lncRNA-disease associations for miRNAs, the disease characteristic matrix, and lncRNAs. Through two attention modules, we enable the model to learn the nonlinear, more complex and useful features of lncRNA, miRNA, and disease characteristic matrix. For the feature embedding matrix composed of lncRNA-disease, the connection between lncRNA-disease feature embedding matrix and lncRNA, miRNA, and disease characteristic matrix was enhanced through deconvolution and feature fusion layer. Compared with several latest methods, the method proposed in this paper can produce better performance. Researches on the cases of osteosarcoma, lung cancer, and gastric cancer have confirmed the effective recognition of potential lncRNA-disease associations.

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Authors and Affiliations

  1. Dalian Key Lab of Digital Technology for National Culture, Dalian Minzu University, Dalian, 116600, China

    Yu Liu & Yingying Yu

  2. Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Darul Ehsan, Malaysia

    Yu Liu

  3. Guangxi Vocational and Technical College, Nanning, 530000, Guangxi, China

    Shimin Zhao

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  1. Yu Liu
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Correspondence to Yu Liu.

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Liu, Y., Yu, Y. & Zhao, S. Dual Attention Mechanisms and Feature Fusion Networks Based Method for Predicting LncRNA-Disease Associations. Interdiscip Sci Comput Life Sci 14, 358–371 (2022). https://doi.org/10.1007/s12539-021-00492-x

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