Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features-Reference-Cited by-同舟云学术

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Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features

Published:2023-07-13 Issue:13 Volume:15 Page:6073-6099
ISSN:1945-4589
Container-title:Aging
language:en
Short-container-title:Aging

Author:

Ribeiro Caio¹,Farmer Christopher K.²,de Magalhães João Pedro³,Freitas Alex A.¹

Affiliation:

1. School of Computing, University of Kent, Canterbury, Kent, UK

2. Centre for Health Services Studies, University of Kent, Canterbury, Kent, UK

3. Genomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK

Publisher

Impact Journals, LLC

Subject

Cell Biology,Aging

Reference69 articles.

1. Disease or not, aging is easily treatable.;Blagosklonny;Aging (Albany NY),2018

2. Aging and age-related diseases: from mechanisms to therapeutic strategies.;Guan;Biogerontology,2021

3. Common genetic associations between age-related diseases.;Thornton;Nat Aging,2021

4. Emerging rejuvenation strategies-Reducing the biological age.;Gladyshev;Aging Cell,2022

5. Translational strategies in aging and age-related disease.;Villeda;Nat Med,2015

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