Human exposure to toxic chemicals presents a huge health burden and disease risk. 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 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 dataset generated using data from the Comparative Toxicogenomics Database. Our case study with bisphenol A (BPA) and its analogues shows that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Overall, our results demonstrate the feasibility and promise of computational predicting environmental chemical-target interactions to efficiently prioritize chemicals for further study.

中文翻译：

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用于暴露目标相互作用预测的矩阵完成数据融合

人类接触有毒化学品会带来巨大的健康负担和疾病风险。了解化学品毒性的关键是了解化学品的分子靶标。由于资源有限，无法对所有化学品进行全面的安全评估，因此用于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，我们实施了一种名为耦合矩阵-矩阵完成 (CMMC) 的新型矩阵完成算法，用于预测暴露组-目标相互作用，该算法利用了大量关于化学暴露及其分子目标的累积数据。我们的方法在使用比较毒物基因组学数据库的数据生成的基准数据集上实现了 0.89 的 AUC。我们对双酚 A (BPA) 及其类似物的案例研究表明，CMMC 可用于准确预测新型化学品的分子靶标，而无需事先了解任何生物活性知识。总的来说，我们的结果证明了计算预测环境化学目标相互作用的可行性和前景，以有效地优先考虑化学物质以供进一步研究。