{"type":"ScholarlyArticle","title":"Learning Data-Driven Drug-Target-Disease Interaction via Neural Tensor Network","authors":[{"name":"Huiyuan Chen","slug":"huiyuan-chen","given":"Huiyuan","family":"Chen","affiliation":"Department of Computer and Data Sciences , Case Western Reserve University"},{"name":"Jing Li","slug":"jing-li","given":"Jing","family":"Li","affiliation":"Department of Computer and Data Sciences , Case Western Reserve University"}],"abstract":"Precise medicine recommendations provide more effective treatments and cause fewer drug side effects. A key step is to understand the mechanistic relationships among drugs, targets, and diseases. Tensor-based models have the ability to explore relationships of drug-target-disease based on large amount of labeled data. However, existing tensor models fail to capture complex nonlinear dependencies among tensor data. In addition, rich medical knowledge are far less studied, which may lead to unsatisfied results. Here we propose a Neural Tensor Network (NeurTN) to assist personalized medicine treatments. NeurTN seamlessly combines tensor algebra and deep neural networks, which offers a more powerful way to capture the nonlinear relationships among drugs, targets, and diseases. To leverage medical knowledge, we augment NeurTN with geometric neural networks to capture the structural information of both drugs’ chemical structures and targets’ sequences. Extensive experiments on real-world datasets demonstrate the effectiveness of the NeurTN model.","doi":"10.24963/ijcai.2020/477","url":"https://ijcai.org/proceedings/2020/477","pdf":"https://ijcai.org/proceedings/2020/0477.pdf","markdown":"https://ijcai.org/proceedings/2020/477.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/477","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":477,"pages":{"first":3452,"last":3458},"keywords":["Machine Learning Applications: Bio/Medicine","Multidisciplinary Topics and Applications: Biology and Medicine","Data Mining: Applications","Multidisciplinary Topics and Applications: AI for Life Science"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25307/file_25307.m3u8","length_sec":283,"provider":"hls"}],"references":[{"position":1,"raw":"[Acar et al., 2011] Evrim Acar, Tamara G Kolda, and Daniel M Dunlavy. 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