Predicting Lifespan-Extending Chemical Compounds for C. Elegans With Machine Learning
07-26-2023“We created datasets for predicting whether or not a compound extends the lifespan of C. elegans [...]”
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BUFFALO, NY- July 26, 2023 – A new research paper was published in Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 15, Issue 13, entitled, “Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features.”
Recently, there has been a growing interest in the development of pharmacological interventions targeting aging, as well as in the use of machine learning for analyzing aging-related data. In this new study, researchers Caio Ribeiro, Christopher K. Farmer, João Pedro de Magalhães, and Alex A. Freitas from the University of Kent and University of Birmingham use machine learning methods to analyze 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 aging-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 analyze these datasets, the researchers 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, they interpreted the most important features in the two best models in light of the biology of aging. One noteworthy feature was the GO term “Glutathione metabolic process”, which plays an important role in cellular redox homeostasis and detoxification. The team 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.”
Read the full paper: DOI: https://doi.org/10.18632/aging.204866
Corresponding Authors: Caio Ribeiro, Alex A. Freitas
Corresponding Emails: C.E.Ribeiro@kent.ac.uk, A.A.Freitas@kent.ac.uk
Keywords: lifespan-extension compounds, longevity drugs, machine learning, feature selection
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About Aging-US:
Aging publishes research papers in all fields of aging research including but not limited, aging from yeast to mammals, cellular senescence, age-related diseases such as cancer and Alzheimer’s diseases and their prevention and treatment, anti-aging strategies and drug development and especially the role of signal transduction pathways such as mTOR in aging and potential approaches to modulate these signaling pathways to extend lifespan. The journal aims to promote treatment of age-related diseases by slowing down aging, validation of anti-aging drugs by treating age-related diseases, prevention of cancer by inhibiting aging. Cancer and COVID-19 are age-related diseases.
Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).
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