Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.
Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.
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DOI：
10.1021/acs.est.4c00458
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发表时间：
2024-03
影响因子：
11.4
通讯作者：
Kai Wang;Nicole Kim;M. Bagherian;Kai Li;Elysia Chou;Justin A. Colacino;Dana C. Dolinoy;Maureen A. Sartor
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文献类型：
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作者：
Kai Wang;Nicole Kim;M. Bagherian;Kai Li;Elysia Chou;Justin A. Colacino;Dana C. Dolinoy;Maureen A. Sartor
人类接触有毒化学物质给健康带来了巨大的负担。了解化学毒性的关键是了解化学物质的分子靶标(S)。由于资源有限，对所有化学品进行全面的安全评估是不可行的，因此，发现环境暴露目标的稳健计算方法是公共卫生研究的一个有前途的方向。在这项研究中，我们实现了一种新的矩阵完成算法，称为耦合矩阵-矩阵完成(CMMC)，用于预测直接和间接曝光组-靶相互作用，该算法利用了大量积累的关于化学暴露及其分子靶标的数据。我们的方法在一个基准数据集上实现了0.89的AUC，该基准数据集使用了来自比较毒理基因组数据库的数据。我们对双酚A及其类似物、全氟辛烷、二恶英、多氯联苯和挥发性有机化合物的案例研究表明，CMMC可以在没有任何事先的生物活性知识的情况下，准确地预测新化学品的分子靶标。我们的结果证明了通过计算预测环境化学物质-目标相互作用的可行性和前景，从而有效地在危险识别和风险评估中对化学物质进行优先排序。
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.