Machine learning for predicting lifespan-extending chemical compounds.

ABSTRACT: Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans' lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound's chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a predic

SUBMITTER: Barardo DG 

PROVIDER: S-EPMC5559171 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature