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Open AccessArticle

Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals

1
Systems Biology of Aging Group, Institute of Biochemistry of the Romanian Academy, 060031 Bucharest, Romania
2
International Longevity Alliance, 92330 Sceaux, France
3
CellFabrik SRL, 060512 Bucharest, Romania
4
The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Center for Multidisciplinary Research on Aging, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
5
SoftServe Inc., 49044 Dnipro, Ukraine
6
Escuela de Postgrado, Pontificia Universidad Católica del Perú, 15023 San Miguel, Peru
*
Author to whom correspondence should be addressed.
These authors contributed equally to the paper as first authors.
Int. J. Mol. Sci. 2021, 22(3), 1073; https://doi.org/10.3390/ijms22031073
Received: 15 December 2020 / Revised: 16 January 2021 / Accepted: 20 January 2021 / Published: 22 January 2021
(This article belongs to the Special Issue Genetics and Epigenetics of Aging and Longevity)
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. View Full-Text
Keywords: transcriptomics; cross-species analysis; maximum lifespan; longevity; mammals transcriptomics; cross-species analysis; maximum lifespan; longevity; mammals
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MDPI and ACS Style

Kulaga, A.Y.; Ursu, E.; Toren, D.; Tyshchenko, V.; Guinea, R.; Pushkova, M.; Fraifeld, V.E.; Tacutu, R. Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals. Int. J. Mol. Sci. 2021, 22, 1073. https://doi.org/10.3390/ijms22031073

AMA Style

Kulaga AY, Ursu E, Toren D, Tyshchenko V, Guinea R, Pushkova M, Fraifeld VE, Tacutu R. Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals. International Journal of Molecular Sciences. 2021; 22(3):1073. https://doi.org/10.3390/ijms22031073

Chicago/Turabian Style

Kulaga, Anton Y.; Ursu, Eugen; Toren, Dmitri; Tyshchenko, Vladyslava; Guinea, Rodrigo; Pushkova, Malvina; Fraifeld, Vadim E.; Tacutu, Robi. 2021. "Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals" Int. J. Mol. Sci. 22, no. 3: 1073. https://doi.org/10.3390/ijms22031073

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