{"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/drug-similarity-integration-through-attentive","title":"Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders","arxiv_id":"1804.10850","date":"2018-04-28","proceeding":null,"authors":["Tengfei Ma","Cao Xiao","Jiayu Zhou","Fei Wang"],"abstract":"Drug similarity has been studied to support downstream clinical tasks such as\ninferring novel properties of drugs (e.g. side effects, indications,\ninteractions) from known properties. The growing availability of new types of\ndrug features brings the opportunity of learning a more comprehensive and\naccurate drug similarity that represents the full spectrum of underlying drug\nrelations. However, it is challenging to integrate these heterogeneous, noisy,\nnonlinear-related information to learn accurate similarity measures especially\nwhen labels are scarce. Moreover, there is a trade-off between accuracy and\ninterpretability. In this paper, we propose to learn accurate and interpretable\nsimilarity measures from multiple types of drug features. In particular, we\nmodel the integration using multi-view graph auto-encoders, and add attentive\nmechanism to determine the weights for each view with respect to corresponding\ntasks and features for better interpretability. Our model has flexible design\nfor both semi-supervised and unsupervised settings. Experimental results\ndemonstrated significant predictive accuracy improvement. Case studies also\nshowed better model capacity (e.g. embed node features) and interpretability.","url_abs":"http://arxiv.org/abs/1804.10850v1","url_pdf":"http://arxiv.org/pdf/1804.10850v1.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":"drug-similarity-integration-through-attentive","repo_url":"https://github.com/matenure/mvgae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}