Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aging01:26

Aging

179
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
179
The Effect of Aging on Tissues01:19

The Effect of Aging on Tissues

2.4K
Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
2.4K
Biological Clocks and Seasonal Responses02:45

Biological Clocks and Seasonal Responses

38.0K
The circadian—or biological—clock is an intrinsic, timekeeping, molecular mechanism that allows plants to coordinate physiological activities over 24-hour cycles called circadian rhythms. Photoperiodism is a collective term for the biological responses of plants to variations in the relative lengths of dark and light periods. The period of light-exposure is called the photoperiod.
38.0K
Mitochondria01:37

Mitochondria

14.9K
Mitochondria are eukaryotic cellular organelles that are known to produce energy through a process called oxidative phosphorylation. Besides their primary function, mitochondria are involved in various cellular processes, including cell growth, differentiation, signaling, metabolism, and senescence. Age-related changes cause a decline in mitochondrial quality and integrity due to increased mitochondrial mutations and oxidative damage. Thus, aging can severely impact mitochondrial functions,...
14.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine learning model for the detection of autism spectrum disorder using electroretinogram signals.

Scientific reports·2026
Same author

Machine learning driven modeling of synergistic perinatal risk profiles in early onset pediatric cerebral palsy.

BMC medical informatics and decision making·2026
Same author

Improving B-cell Linear Epitope Prediction <i>via</i> Multiple Feature Fusion and an Integrated Machine Learning Algorithm.

Current drug targets·2026
Same author

AVSeg-XAI: Deep learning framework for A/V segmentation with vascular features reveals retinal oculomics as biomarker for cardiovascular disease.

BioData mining·2026
Same author

Machine learning based model for the detection of multiple sclerosis from OCT-derived macular and optic disc retinal biomarkers.

BMC medical informatics and decision making·2026
Same author

Correction: Annotation of nuclear lncRNAs based on chromatin interactions.

PloS one·2026

Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Automated Lifespan Monitoring &#8211; Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.2K

Deep aging clocks: AI-powered strategies for biological age estimation.

Luma Srour1, Yosra Bejaoui1, James She2

  • 1College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.

Ageing Research Reviews
|September 3, 2025
PubMed
Summary

New deep aging clocks leverage AI to accurately measure biological age, enhancing health span and longevity research. These advanced tools overcome limitations of traditional methods for better health outcomes.

Keywords:
Aging clocksBiological agingDeep learningEpigeneticsMicrobiomeRetinal imagesTranscriptomics

More Related Videos

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells
12:24

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

Published on: May 6, 2009

16.8K
Automated Analysis of C. elegans Swim Behavior Using CeleST Software
08:47

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

Published on: December 7, 2016

12.8K

Related Experiment Videos

Last Updated: Sep 9, 2025

Author Spotlight: Automated Lifespan Monitoring &#8211; Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.2K
Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells
12:24

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

Published on: May 6, 2009

16.8K
Automated Analysis of C. elegans Swim Behavior Using CeleST Software
08:47

Automated Analysis of C. elegans Swim Behavior Using CeleST Software

Published on: December 7, 2016

12.8K

Area of Science:

  • Gerontology and Bioinformatics
  • Biotechnology and AI in Health

Background:

  • The aging population necessitates strategies to enhance health and lifespan.
  • Biological age assessment is crucial for developing and evaluating longevity interventions.
  • Existing aging clocks often assume linear aging, which may not accurately reflect complex biological changes.

Purpose of the Study:

  • To review current deep aging clocks for biological age prediction.
  • To highlight advancements in artificial intelligence (AI) and deep learning for aging research.
  • To discuss the potential of deep aging clocks in improving health span and longevity.

Main Methods:

  • Summarizing various deep aging clock approaches: epigenetics, transcriptomics, metabolomics, microbiome, and imaging.
  • Reviewing the application of deep learning techniques in biological age prediction.
  • Analyzing the advantages of deep aging clocks over traditional linear models.

Main Results:

  • Deep aging clocks demonstrate enhanced accuracy in predicting biological age.
  • AI and deep learning significantly improve the predictive power of aging clocks.
  • Diverse data types (epigenetics, transcriptomics, etc.) contribute to robust aging predictions.

Conclusions:

  • Deep aging clocks offer a more nuanced assessment of biological aging.
  • AI-driven deep aging clocks are pivotal for advancing longevity science.
  • These tools promise to accelerate the development of interventions for longer, healthier lives.