{"type":"ScholarlyArticle","title":"Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction","authors":[{"name":"Kexin Zhang","slug":"kexin-zhang","given":"Kexin","family":"Zhang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Feng Huang","slug":"feng-huang","given":"Feng","family":"Huang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Luotao Liu","slug":"luotao-liu","given":"Luotao","family":"Liu","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Zhankun Xiong","slug":"zhankun-xiong","given":"Zhankun","family":"Xiong","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Hongyu Zhang","slug":"hongyu-zhang","given":"Hongyu","family":"Zhang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China; Hubei Key Laboratory of Agricultural Bioinformatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Yuan Quan","slug":"yuan-quan","given":"Yuan","family":"Quan","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China; Hubei Key Laboratory of Agricultural Bioinformatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Wen Zhang","slug":"wen-zhang","given":"Wen","family":"Zhang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China; Hubei Key Laboratory of Agricultural Bioinformatics , Huazhong Agricultural University , Wuhan 430070 , China"}],"abstract":"The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure.","doi":"10.24963/ijcai.2024/681","url":"https://ijcai.org/proceedings/2024/681","pdf":"https://ijcai.org/proceedings/2024/0681.pdf","markdown":"https://ijcai.org/proceedings/2024/681.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/681","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":681,"pages":{"first":6161,"last":6169},"keywords":["Multidisciplinary Topics and Applications: MTA: Bioinformatics","Multidisciplinary Topics and Applications: MTA: Health and medicine"],"videos":[],"references":[{"position":1,"raw":"[Ata et al., 2021] Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, and Xiao-Li Li. 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