{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-gene-expression-convolutions-using","title":"Towards Gene Expression Convolutions using Gene Interaction Graphs","arxiv_id":"1806.06975","date":"2018-06-18","proceeding":null,"authors":["Francis Dutil","Joseph Paul Cohen","Martin Weiss","Georgy Derevyanko","Yoshua Bengio"],"abstract":"We study the challenges of applying deep learning to gene expression data. We\nfind experimentally that there exists non-linear signal in the data, however is\nit not discovered automatically given the noise and low numbers of samples used\nin most research. We discuss how gene interaction graphs (same pathway,\nprotein-protein, co-expression, or research paper text association) can be used\nto impose a bias on a deep model similar to the spatial bias imposed by\nconvolutions on an image. We explore the usage of Graph Convolutional Neural\nNetworks coupled with dropout and gene embeddings to utilize the graph\ninformation. We find this approach provides an advantage for particular tasks\nin a low data regime but is very dependent on the quality of the graph used. We\nconclude that more work should be done in this direction. We design experiments\nthat show why existing methods fail to capture signal that is present in the\ndata when features are added which clearly isolates the problem that needs to\nbe addressed.","url_abs":"http://arxiv.org/abs/1806.06975v1","url_pdf":"http://arxiv.org/pdf/1806.06975v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-gene-expression-convolutions-using","repo_url":"https://github.com/mila-iqia/gene-graph-conv","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}