Gene Target Prediction of Environmental Chemicals Using Coupled Matrix–Matrix Completion

Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for disc...

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Bibliographic Details
Published in:Environmental science & technology 2024-04, Vol.58 (13), p.5889-5898
Main Authors: Wang, Kai, Kim, Nicole, Bagherian, Maryam, Li, Kai, Chou, Elysia, Colacino, Justin A., Dolinoy, Dana C., Sartor, Maureen A.
Format: Article
Language:English
Subjects:
Algorithms
Biocompatibility
Biological activity
Bisphenol A
Chemicals
Chlorinated hydrocarbons
Data Science
Dioxins
Environmental Exposure - analysis
Exposure
Hazard identification
Humans
Polychlorinated Biphenyls - analysis
Public Health
Risk Assessment
Toxicity
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Summary:Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix–matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.
ISSN:0013-936X
1520-5851
1520-5851
DOI:10.1021/acs.est.4c00458