{"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/inferring-multidimensional-rates-of-aging","title":"Inferring Multidimensional Rates of Aging from Cross-Sectional Data","arxiv_id":"1807.04709","date":"2018-07-12","proceeding":null,"authors":["Emma Pierson","Pang Wei Koh","Tatsunori Hashimoto","Daphne Koller","Jure Leskovec","Nicholas Eriksson","Percy Liang"],"abstract":"Modeling how individuals evolve over time is a fundamental problem in the\nnatural and social sciences. However, existing datasets are often\ncross-sectional with each individual observed only once, making it impossible\nto apply traditional time-series methods. Motivated by the study of human\naging, we present an interpretable latent-variable model that learns temporal\ndynamics from cross-sectional data. Our model represents each individual's\nfeatures over time as a nonlinear function of a low-dimensional,\nlinearly-evolving latent state. We prove that when this nonlinear function is\nconstrained to be order-isomorphic, the model family is identifiable solely\nfrom cross-sectional data provided the distribution of time-independent\nvariation is known. On the UK Biobank human health dataset, our model\nreconstructs the observed data while learning interpretable rates of aging\nassociated with diseases, mortality, and aging risk factors.","url_abs":"http://arxiv.org/abs/1807.04709v3","url_pdf":"http://arxiv.org/pdf/1807.04709v3.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":"inferring-multidimensional-rates-of-aging","repo_url":"https://github.com/epierson9/multiphenotype_methods","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"human-aging","task_name":"Human Aging"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.04709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.04709"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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