ArticleInternational journal of molecular sciences2021
Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Unlocking longevity through the comparative biology of aging.Nature aging · 2025Review
- Molecular signatures of premature aging in Major Depression and Substance Use Disorders.Scientific data · 2024Article
- Single-cell profiling reveals inflammatory polarization of human carotid versus femoral plaque leukocytes.JCI insight · 2023Article
- Gene expressions associated with longer lifespan and aging exhibit similarity in mammals.Nucleic acids research · 2023Article
- Towards AI-driven longevity research: An overview.Frontiers in aging · 2023Review
- Systems biology analysis of lung fibrosis-related genes in the bleomycin mouse model.Scientific reports · 2021Article
- Knock-down ofAging · 2021Article
- Footprints in the Sand: Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
One of the important questions in aging research is how differences in transcriptomics are associated with the longevity of various species. Unfortunately, at the level of individual genes, the links between expression in different organs and maximum lifespan (MLS) are yet to be fully understood. Analyses are complicated further by the fact that MLS is highly associated with other confounding factors (metabolic rate, gestation period, body mass, etc.) and that linear models may be limiting. Using gene expression from 41 mammalian species, across five organs, we constructed gene-centric regression models associating gene expression with MLS and other species traits. Additionally, we used SHapley Additive exPlanations and Bayesian networks to investigate the non-linear nature of the interrelations between the genes predicted to be determinants of species MLS. Our results revealed that expression patterns correlate with MLS, some across organs, and others in an organ-specific manner. The combination of methods employed revealed gene signatures formed by only a few genes that are highly predictive towards MLS, which could be used to identify novel longevity regulator candidates in mammals.
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Registered trials
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