[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-427295-105":59,"doc-detail-427295-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","multi-omic-age-associations-identified-with-artificial-intelligence-ai-read-online-free","MULTI-OMIC AGE ASSOCIATIONS IDENTIFIED WITH ARTIFICIAL INTELLIGENCE (AI) - read online free","","Research explores machine learning and artificial intelligence methods to integrate multi-omics data and uncover molecular factors linked to aging. Multi-omic AI models can estimate “biological age” and reveal age-associated molecular features, but prior tabular deep learning has not consistently beat traditional approaches such as elastic-net. Using a transformer-based tabular foundation model (TabPFN) with in-context learning, proteomics and metabolomics were integrated in the MrOS cohort. In 493 men, TabPFN predicted age with lower test RMSE (2.75 vs 3.1). SHAP highlights Pleiotrophin, Frizzled-7, secreted frizzled-related protein 1, and N-acetylcarnosine. Results support biologically relevant features and improved performance over traditional ML, with future work in replication and females.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multi-omic-age-associations-identified-with-artificial-intelligence-ai-read-online-free/427295/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multi-omic-age-associations-identified-with-artificial-intelligence-ai-read-online-free/427295.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-16","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address?","Question",{"text":112,"@type":113},"It targets how machine learning and AI can integrate multi-omics data to identify molecular factors associated with aging and support biological age prediction.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the TabPFN model used?",{"text":117,"@type":113},"A tabular foundation AI model (TabPFN) based on transformer in-context learning was trained to integrate proteomics and metabolomics for age prediction.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main performance results and which features were highlighted?",{"text":121,"@type":113},"On 493 men, TabPFN achieved a test RMSE of 2.75 years versus 3.1 for elastic-net, and SHAP identified Pleiotrophin, Frizzled-7, secreted frizzled-related protein 1, and N-acetylcarnosine as contributing proteins/metabolites.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},427295,1791039200,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","142 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.473  \nMULTI-OMIC AGE ASSOCIATIONS IDENTIFIED WITH ARTIFICIAL INTELLIGENCE (AI)  \nDaniel Evans1, Jiashun Zheng1, Peggy Cawthon2,  \nSteven Cummings2, and Hao Li1, 1. University of California, San Francisco, San Francisco, California, United States,  \n2. California Pacific Medical Center, San Francisco, California, United States  \nThere is substantial interest in using Machine Learning (ML) and Artificial Intelligence (AI) approaches to integrate multi-omics data to identify molecular factors associated with age. Multi-omic AI models can be used to predict “biological age”and to identify age-associated molecular features. To-date, predictions from traditional ML methods like elastic-net have outperformed AI models like deep learning with tabular (spreadsheet-like) data. However, new developments in transformer architectures have led to AI models that can outperform traditional ML methods. In this work, a tabular foundation AI model (TabPFN) built on transformer-based in-context learning (ICL) algorithms is used to integrate proteomics and metabolomics to identify models forage in a cohort study of older men, the Osteoporotic Fracturesin Men (MrOS) Study. The serum proteomics assay was the SomaLogic 7K panel, and the serum metabolomics assay was the Metabolon Discovery HD platform. In 493 male participants split into training and test partitions, TabPFN models for age adjusted for body mass index (BMI) and estimated glomerular filtration rate (eGFR) achieved a root mean square error (RMSE) in the test set of 2.75 years, which outperformed an elastic-net model RMSE in the test set of 3.1 years. Proteins and metabolites contributing to the TabPFN model for age identified by Shapley Additive exPlanations (SHAP) included Pleiotrophin, Frizzled-7, secreted frizzled-related protein 1, and N-acetylcarnosine. Multi-omic AI models outperformed traditional ML models forage and identified biologically relevant features. Future directions include replication of findings in other studies and assessment of model performance in females.","cbCailuY50rrT9DL","https://ap.wps.com/l/cbCailuY50rrT9DL","pdf",187900,"English","# Abstract citation ID: igaf122.473\n## Background and motivation\n## Methods: TabPFN and in-context learning\n## Study design and assays\n## Results and model performance\n## Interpretable features with SHAP\n## Future directions","[{\"question\":\"What problem does the study address?\",\"answer\":\"It targets how machine learning and AI can integrate multi-omics data to identify molecular factors associated with aging and support biological age prediction.\"},{\"question\":\"How was the TabPFN model used?\",\"answer\":\"A tabular foundation AI model (TabPFN) based on transformer in-context learning was trained to integrate proteomics and metabolomics for age prediction.\"},{\"question\":\"What were the main performance results and which features were highlighted?\",\"answer\":\"On 493 men, TabPFN achieved a test RMSE of 2.75 years versus 3.1 for elastic-net, and SHAP identified Pleiotrophin, Frizzled-7, secreted frizzled-related protein 1, and N-acetylcarnosine as contributing proteins/metabolites.\"}]","MULTI-OMIC AGE ASSOCIATIONS IDENTIFIED WITH ARTIFICIAL INTELLIGENCE (AI) - read online free | PDF",1790643429]