Open AccessIdentification of drug combinations on the basis of machine learning to maximize anti-aging effects
Author(s) -
Sun Kyung Kim,
Peter C. Goughnour,
Eui Jin Lee,
Myeong Hyun Kim,
Hee Jin Chae,
Gwang Yeul Yun,
Yi Rang Kim,
Jin Woo Choi
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0246106
Subject(s) - in silico , drug repositioning , machine learning , drug , computational biology , drug discovery , artificial intelligence , identification (biology) , senescence , computer science , artificial neural network , deep learning , bioinformatics , gene , biology , genetics , pharmacology , botany
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 the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.