{"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/integrate-multi-omic-data-using-affinity","title":"Integrate Multi-omic Data Using Affinity Network Fusion (ANF) for Cancer Patient Clustering","arxiv_id":"1708.07136","date":"2017-08-23","proceeding":null,"authors":[],"abstract":"Clustering cancer patients into subgroups and identifying cancer subtypes is\nan important task in cancer genomics. Clustering based on comprehensive\nmulti-omic molecular profiling can often achieve better results than those\nusing a single data type, since each omic data type (representing one view of\npatients) may contain complementary information. However, it is challenging to\nintegrate heterogeneous omic data types directly. Based on one popular method\n-- Similarity Network Fusion (SNF), we presented Affinity Network Fusion (ANF)\nin this paper, an \"upgrade\" of SNF with several advantages. Similar to SNF, ANF\ntreats each omic data type as one view of patients and learns a fused affinity\n(transition) matrix for clustering. We applied ANF to a carefully processed\nharmonized cancer dataset downloaded from GDC data portals consisting of 2193\npatients, and generated promising results on clustering patients into correct\ndisease types. Our experimental results also demonstrated the power of feature\nselection and transformation combined with using ANF in patient clustering.\nMoreover, eigengap analysis suggests that the learned affinity matrices of four\ncancer types using our proposed framework may have successfully captured\npatient group structure and can be used for discovering unknown cancer\nsubtypes.","url_abs":"http://arxiv.org/abs/1708.07136v1","url_pdf":"http://arxiv.org/pdf/1708.07136v1.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":"integrate-multi-omic-data-using-affinity","repo_url":"https://github.com/BeautyOfWeb/ANF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}