{"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/bayesian-hybrid-matrix-factorisation-for-data","title":"Bayesian Hybrid Matrix Factorisation for Data Integration","arxiv_id":"1704.04962","date":"2017-04-17","proceeding":null,"authors":["Thomas Brouwer","Pietro Lió"],"abstract":"We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for\ndata integration, based on combining multiple matrix factorisation methods,\nthat can be used for in- and out-of-matrix prediction of missing values. The\nmodel is very general and can be used to integrate many datasets across\ndifferent entity types, including repeated experiments, similarity matrices,\nand very sparse datasets. We apply our method on two biological applications,\nand extensively compare it to state-of-the-art machine learning and matrix\nfactorisation models. For in-matrix predictions on drug sensitivity datasets we\nobtain consistently better performances than existing methods. This is\nespecially the case when we increase the sparsity of the datasets. Furthermore,\nwe perform out-of-matrix predictions on methylation and gene expression\ndatasets, and obtain the best results on two of the three datasets, especially\nwhen the predictivity of datasets is high.","url_abs":"http://arxiv.org/abs/1704.04962v1","url_pdf":"http://arxiv.org/pdf/1704.04962v1.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":"bayesian-hybrid-matrix-factorisation-for-data","repo_url":"https://github.com/ThomasBrouwer/HMF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bayesian-hybrid-matrix-factorisation-for-data","repo_url":"https://github.com/ThomasBrouwer/BNMTF_ARD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-integration","task_name":"Data Integration"},{"task_slug":"missing-values","task_name":"Missing Values"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}