Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

ABSTRACT: Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified t

SUBMITTER: Kim SK 

PROVIDER: S-EPMC7843016 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature