{"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/learning-genomic-representations-to-predict","title":"Learning Genomic Representations to Predict Clinical Outcomes in Cancer","arxiv_id":"1609.08663","date":"2016-09-27","proceeding":null,"authors":["Safoora Yousefi","Congzheng Song","Nelson Nauata","Lee Cooper"],"abstract":"Genomics are rapidly transforming medical practice and basic biomedical\nresearch, providing insights into disease mechanisms and improving therapeutic\nstrategies, particularly in cancer. The ability to predict the future course of\na patient's disease from high-dimensional genomic profiling will be essential\nin realizing the promise of genomic medicine, but presents significant\nchallenges for state-of-the-art survival analysis methods. In this abstract we\npresent an investigation in learning genomic representations with neural\nnetworks to predict patient survival in cancer. We demonstrate the advantages\nof this approach over existing survival analysis methods using brain tumor\ndata.","url_abs":"http://arxiv.org/abs/1609.08663v1","url_pdf":"http://arxiv.org/pdf/1609.08663v1.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":"learning-genomic-representations-to-predict","repo_url":"https://github.com/leondepf/SurvivalNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}