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

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

SUBMITTER: Ribeiro C 

PROVIDER: S-EPMC10373959 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature