Deep aging clocks: AI-powered strategies for biological age estimation
<p>Several strategies have emerged lately in response to the rapid increase in the aging population to enhance health and life span and manage aging challenges. Developing such strategies is imperative and requires an assessment of biological aging. Several aging clocks have recently been deve...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1864513539908042752 |
|---|---|
| author | Luma Srour (22254409) |
| author2 | Yosra Bejaoui (8552574) James She (17725974) Tanvir Alam (638619) Nady El Hajj (686554) |
| author2_role | author author author author |
| author_facet | Luma Srour (22254409) Yosra Bejaoui (8552574) James She (17725974) Tanvir Alam (638619) Nady El Hajj (686554) |
| author_role | author |
| dc.creator.none.fl_str_mv | Luma Srour (22254409) Yosra Bejaoui (8552574) James She (17725974) Tanvir Alam (638619) Nady El Hajj (686554) |
| dc.date.none.fl_str_mv | 2025-09-04T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.arr.2025.102889 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_aging_clocks_AI-powered_strategies_for_biological_age_estimation/30135322 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Biological aging Aging clocks Deep learning Retinal images Epigenetics Transcriptomics Microbiome |
| dc.title.none.fl_str_mv | Deep aging clocks: AI-powered strategies for biological age estimation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Several strategies have emerged lately in response to the rapid increase in the aging population to enhance health and life span and manage aging challenges. Developing such strategies is imperative and requires an assessment of biological aging. Several aging clocks have recently been developed to measure biological aging and to assess the efficacy of longevity interventions. Biological age better reflects a person’s actual age and is closely associated with health outcomes and time to mortality. Traditionally, most aging clocks assume that biological changes occur linearly over time. However, age-related changes do not necessarily follow a linear trajectory. Thus, “Deep Aging Clocks” have been developed to overcome previous clocks' limitations and better capture subtle changes that occur during aging. Here, we summarize the current deep aging clocks, including epigenetics, transcriptomics, metabolomics, microbiome, and imaging based clocks for age prediction. Recent advances in artificial intelligence (AI), utilizing deep learning techniques, have significantly enhanced the prediction of biological aging, and this would help improve aging clocks and accelerate efforts to reach longer and healthier lives.</p><h2>Other Information</h2> <p> Published in: Ageing Research Reviews<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.arr.2025.102889" target="_blank">https://dx.doi.org/10.1016/j.arr.2025.102889</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c0934d02a76a3b69ffbc502d40cf37c3 |
| identifier_str_mv | 10.1016/j.arr.2025.102889 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30135322 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep aging clocks: AI-powered strategies for biological age estimationLuma Srour (22254409)Yosra Bejaoui (8552574)James She (17725974)Tanvir Alam (638619)Nady El Hajj (686554)Biological sciencesBioinformatics and computational biologyBiomedical and clinical sciencesClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceBiological agingAging clocksDeep learningRetinal imagesEpigeneticsTranscriptomicsMicrobiome<p>Several strategies have emerged lately in response to the rapid increase in the aging population to enhance health and life span and manage aging challenges. Developing such strategies is imperative and requires an assessment of biological aging. Several aging clocks have recently been developed to measure biological aging and to assess the efficacy of longevity interventions. Biological age better reflects a person’s actual age and is closely associated with health outcomes and time to mortality. Traditionally, most aging clocks assume that biological changes occur linearly over time. However, age-related changes do not necessarily follow a linear trajectory. Thus, “Deep Aging Clocks” have been developed to overcome previous clocks' limitations and better capture subtle changes that occur during aging. Here, we summarize the current deep aging clocks, including epigenetics, transcriptomics, metabolomics, microbiome, and imaging based clocks for age prediction. Recent advances in artificial intelligence (AI), utilizing deep learning techniques, have significantly enhanced the prediction of biological aging, and this would help improve aging clocks and accelerate efforts to reach longer and healthier lives.</p><h2>Other Information</h2> <p> Published in: Ageing Research Reviews<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.arr.2025.102889" target="_blank">https://dx.doi.org/10.1016/j.arr.2025.102889</a></p>2025-09-04T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.arr.2025.102889https://figshare.com/articles/journal_contribution/Deep_aging_clocks_AI-powered_strategies_for_biological_age_estimation/30135322CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301353222025-09-04T09:00:00Z |
| spellingShingle | Deep aging clocks: AI-powered strategies for biological age estimation Luma Srour (22254409) Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Biological aging Aging clocks Deep learning Retinal images Epigenetics Transcriptomics Microbiome |
| status_str | publishedVersion |
| title | Deep aging clocks: AI-powered strategies for biological age estimation |
| title_full | Deep aging clocks: AI-powered strategies for biological age estimation |
| title_fullStr | Deep aging clocks: AI-powered strategies for biological age estimation |
| title_full_unstemmed | Deep aging clocks: AI-powered strategies for biological age estimation |
| title_short | Deep aging clocks: AI-powered strategies for biological age estimation |
| title_sort | Deep aging clocks: AI-powered strategies for biological age estimation |
| topic | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Biological aging Aging clocks Deep learning Retinal images Epigenetics Transcriptomics Microbiome |