{"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/discovering-and-deciphering-relationships","title":"Discovering and Deciphering Relationships Across Disparate Data Modalities","arxiv_id":"1609.05148","date":"2016-09-16","proceeding":null,"authors":["Joshua T. Vogelstein","Eric Bridgeford","Qing Wang","Carey E. Priebe","Mauro Maggioni","Cencheng Shen"],"abstract":"Understanding the relationships between different properties of data, such as\nwhether a connectome or genome has information about disease status, is\nbecoming increasingly important in modern biological datasets. While existing\napproaches can test whether two properties are related, they often require\nunfeasibly large sample sizes in real data scenarios, and do not provide any\ninsight into how or why the procedure reached its decision. Our approach,\n\"Multiscale Graph Correlation\" (MGC), is a dependence test that juxtaposes\npreviously disparate data science techniques, including k-nearest neighbors,\nkernel methods (such as support vector machines), and multiscale analysis (such\nas wavelets). Other methods typically require double or triple the number\nsamples to achieve the same statistical power as MGC in a benchmark suite\nincluding high-dimensional and nonlinear relationships - spanning polynomial\n(linear, quadratic, cubic), trigonometric (sinusoidal, circular, ellipsoidal,\nspiral), geometric (square, diamond, W-shape), and other functions, with\ndimensionality ranging from 1 to 1000. Moreover, MGC uniquely provides a simple\nand elegant characterization of the potentially complex latent geometry\nunderlying the relationship, providing insight while maintaining computational\nefficiency. In several real data applications, including brain imaging and\ncancer genetics, MGC is the only method that can both detect the presence of a\ndependency and provide specific guidance for the next experiment and/or\nanalysis to conduct.","url_abs":"http://arxiv.org/abs/1609.05148v8","url_pdf":"http://arxiv.org/pdf/1609.05148v8.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":"discovering-and-deciphering-relationships","repo_url":"https://github.com/NeuroDataDesign/mgcpy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"discovering-and-deciphering-relationships","repo_url":"https://github.com/neurodata/mgc-r","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"discovering-and-deciphering-relationships","repo_url":"https://github.com/neurodata/mgcpy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"discovering-and-deciphering-relationships","repo_url":"https://github.com/neurodata/r-mgc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}