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Research on Efficient and Low-cost Drug-disease Association Prediction Method Based on Dual Attention in Heterogeneous Networks

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DOI: 10.23977/medbm.2024.020108 | Downloads: 6 | Views: 171

Author(s)

Yujie Yang 1, Yue Gao 1, Xiaohan Li 1, Wenhao Ding 1, Chengyang Gao 1

Affiliation(s)

1 School of Software, Yunnan University, Kunming, 650500, China

Corresponding Author

Yujie Yang

ABSTRACT

Drug development usually costs a high cost, so it is very important to establish an efficient, low-cost and accurate prediction method of drug-disease correlation. In this paper, a drug-disease prediction method based on dual attention in heterogeneous networks is proposed. First, the experimental data set is constructed through the biological database, then the node feature information in the heterogeneous network is extracted by the graph attention network, and the node feature information is filtered and enhanced by SENet. Finally, through the 10% discount cross verification evaluation, GASEDDA achieved an accuracy of 98.5%.

KEYWORDS

Heterogeneous network, drug-disease association, attention mechanism, graph attention, SENet

CITE THIS PAPER

Yujie Yang, Yue Gao, Xiaohan Li, Wenhao Ding, Chengyang Gao, Research on Efficient and Low-cost Drug-disease Association Prediction Method Based on Dual Attention in Heterogeneous Networks. MEDS Basic Medicine (2024) Vol. 2: 56-64. DOI: http://dx.doi.org/10.23977/medbm.2024.020108.

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