Skip to main content

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

Ribeiro, Caio, Farmer, Christopher K., de Magalhães, João Pedro, Freitas, Alex A. (2023) Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features. Aging, 15 (13). pp. 6073-6099. E-ISSN 1945-4589. (doi:10.18632/aging.204866) (KAR id:102283)

Abstract

Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four types of datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on: compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase’s Phenotype Ontology. To analyse these datasets, we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. In addition, we interpreted the most important features in the two best models in light of the biology of ageing. One noteworthy feature was the GO term “Glutathione metabolic process”, which plays an important role in cellular redox homeostasis and detoxification. We also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds. These include nitroprusside, which is used as an antihypertensive medication. Overall, our work opens avenues for future work in employing machine learning to predict novel life-extending compounds.

Item Type: Article
DOI/Identification number: 10.18632/aging.204866
Additional information: For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
Uncontrolled keywords: cell biology; aging
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: Biotechnology and Biological Sciences Research Council (https://ror.org/00cwqg982)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 03 Aug 2023 13:50 UTC
Last Modified: 09 Jan 2024 12:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/102283 (The current URI for this page, for reference purposes)

University of Kent Author Information

Ribeiro, Caio.

Creator's ORCID:
CReDIT Contributor Roles:

Farmer, Christopher K..

Creator's ORCID: https://orcid.org/0000-0003-1736-8242
CReDIT Contributor Roles:

Freitas, Alex A..

Creator's ORCID: https://orcid.org/0000-0001-9825-4700
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.