{"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-a-generative-model-of-cancer","title":"Learning a Generative Model of Cancer Metastasis","arxiv_id":"1901.06023","date":"2019-01-17","proceeding":null,"authors":["Benjamin Kompa","Beau Coker"],"abstract":"We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer\nGenome Atlas (TCGA). We demonstrate that the UFDN learns a biologically\nrelevant latent space of gene expression data by applying our network to two\nclassification tasks of cancer status and cancer type. Our UFDN specific\nalgorithms perform comparably to random forest methods. The UFDN allows for\ncontinuous, partial interpolation between distinct cancer types. Furthermore,\nwe perform an analysis of differentially expressed genes between skin cutaneous\nmelanoma(SKCM) samples and the same samples interpolated into glioblastoma\n(GBM). We demonstrate that our interpolations learn relevant metagenes that\nrecapitulate known glioblastoma mechanisms and suggest possible starting points\nfor investigations into the metastasis of SKCM into GBM.","url_abs":"http://arxiv.org/abs/1901.06023v1","url_pdf":"http://arxiv.org/pdf/1901.06023v1.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-a-generative-model-of-cancer","repo_url":"https://github.com/bkompa/UFDN-TCGA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"disentanglement","task_name":"Disentanglement"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}