{"type":"ScholarlyArticle","title":"Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification","authors":[{"name":"Sungmin Rhee","slug":"sungmin-rhee","given":"Sungmin","family":"Rhee","affiliation":"Department of Computer Science and Engineering , Seoul National University"},{"name":"Seokjun Seo","slug":"seokjun-seo","given":"Seokjun","family":"Seo","affiliation":"Department of Computer Science and Engineering , Seoul National University"},{"name":"Sun Kim","slug":"sun-kim","given":"Sun","family":"Kim","affiliation":"Department of Computer Science and Engineering , Seoul National University; Bioinformatics Institute , Seoul National University , Seoul , Republic of Korea; Interdisciplinary Program in Bioinformatics , Seoul National University"}],"abstract":"Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown an ability to address the problem like this. However, it conventionally used grid-like structured data, thus application of deep learning technologies to the human disease subtypes is yet to be explored. To overcome the issue, we propose a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). Experimental results on synthetic data and breast cancer data demonstrate that our proposed method shows better performances than existing methods.","doi":"10.24963/ijcai.2018/490","url":"https://ijcai.org/proceedings/2018/490","pdf":"https://ijcai.org/proceedings/2018/0490.pdf","markdown":"https://ijcai.org/proceedings/2018/490.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/490","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":490,"pages":{"first":3527,"last":3534},"keywords":["Machine Learning: Classification","Machine Learning Applications: Bio;Medicine","Multidisciplinary Topics and Applications: Biology and Medicine","Machine Learning: Learning Graphical Models","Machine Learning: Deep Learning","Machine Learning: Relational Learning"],"videos":[],"references":[{"position":1,"raw":"[Barabasi and Oltvai, 2004] Albert-Laszlo Barabasi and Zoltan N Oltvai. 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