Concepedia

Aging Clock Models

Aging Clock Models is a computational or mathematical framework that belongs to the class of biomarkers of aging, distinguished by its ability to quantitatively estimate biological age based on molecular, cellular, or physiological data, often leveraging machine learning or statistical algorithms trained on longitudinal or cross-sectional age-related changes.

37

Publications

3K

Citations

238

Authors

124

Institutions

Publications per year

2017–2026

35

Variants

Biological Aging Clocks

Parents

Publications

37

An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Nazish Sayed, Yingxiang Huang, Khiem Nguyen et al. · Nature Aging · 2021 · 589 citations · Full text

, , +14

Distinct biological ages of organs and systems identified from a multi-omics study

Chao Nie, Yan Li, Rui Li et al. · Cell Reports · 2022 · 231 citations · Full text

, , +17

Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations

Polina Mamoshina, Kirill Kochetov, Franco Cortese et al. · The Journals of Gerontology Series A · 2018 · 194 citations · Full text

, , +17

BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy

David H. Meyer, Björn Schumacher · Aging Cell · 2021 · 172 citations · Full text

, , +17

An epigenetic aging clock for dogs and wolves

Michael J. Thompson, Bridgett M. vonHoldt, Steve Horvath et al. · Aging · 2017 · 162 citations · Full text

, , +17

1–5 of 37

Authors

238

  1. AZ
    Alex Zhavoronkov

    Johns Hopkins University, United States

    5 publications · 495 citations

    5

    h-index

  2. KK
    Kirill Kochetov

    Buck Institute for Research on Aging, United States

    4 publications · 438 citations

    4

    h-index

  3. DH
    David H. Meyer

    University of Cologne, Germany

    3 publications · 299 citations

    3

    h-index

  4. CF
    Claudio Franceschi

    University of Bologna, Italy

    3 publications · 891 citations

    3

    h-index

  5. BS
    Björn Schumacher

    Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases, Germany

    3 publications · 299 citations

    3

    h-index

1–5 of 238

Institutions

124

  1. Stanford University

    Stanford, United States

    7 h-index

    27 publications

    8.3K citations

  2. Buck Institute for Research on Aging

    Novato, United States

    6 h-index

    11 publications

    2.8K citations

  3. Harvard University

    Cambridge, United States

    5 h-index

    31 publications

    1.6K citations

  4. University of Cologne

    Cologne, Germany

    4 h-index

    7 publications

    620 citations

  5. National University of Singapore

    Singapore, Singapore

    4 h-index

    12 publications

    680 citations

  6. Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases

    Cologne, Germany

    4 h-index

    7 publications

    620 citations

  7. Center for Life Sciences

    Beijing, China

    3 h-index

    4 publications

    645 citations

  8. N. I. Lobachevsky State University of Nizhny Novgorod

    Nizhny Novgorod, Russia

    3 h-index

    4 publications

    962 citations

1–8 of 124

Venues

  1. Aging

    5 publications · 359 citations

    5

    h-index

  2. Nature Aging

    4 publications · 810 citations

    4

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 97 citations

    3

    h-index

  4. Aging Cell

    2 publications · 283 citations

    2

    h-index

  5. Science Advances

    2 publications · 191 citations

    2

    h-index

1–5