Improving chemical exposome target prediction by application of Coupled Matrix/Tensor-Matrix/Tensor Completion algorithms

项目总结 曝光体的定义是公众接触到的所有曝光物，包括 有毒化学物质。接触这些化学品给人类健康和疾病带来了巨大的负担。 由于时间和时间的限制，对所有新化学品进行全面的安全评估是困难的 资金。然而，由于与数以千计的暴露和他们的 分子靶标，我们假设可以开发出计算方法来准确预测 新化学品的分子作用和靶标。在这项建议中，我们建议实施和应用 一种新的矩阵补全算法--耦合矩阵/张量矩阵补全算法(CM/TMC) 和耦合矩阵/张量-张量完成(CM/TTC)来预测分子靶标和 环境化学物质大规模暴露的靶组织。建议的研究将是 通过以下具体目标完成：1)将CM/TMC算法应用于 与暴露相关的数据集，将结果与替代方法进行比较，2)优化CM/TMC方法以 暴露靶组织预测，以及3)发展暴露靶预测的CM/TTC方法， 执行实验验证，并建立用于曝光目标预测的网络门户。本研究 提出了第一种基于矩阵补全的曝光分子靶标预测方法 组织预测。获奖导师(K99)阶段的主要目标是为候选人提供 接受数据科学和毒理学方面的额外培训，以获得科学独立性和 成功地完成了他的职业目标。K99阶段将在伊利诺伊大学进行 密歇根(UM)，在Maureen Sartor博士、Justin Colacino、Kayvan Najarian和 马里奥·梅德韦多维奇，他们都是各自领域的专家。一个跨学科的顾问团队将 协助应聘者进行研究和职业发展。在K99阶段完成后， 候选人将为成为一名独立调查员做好充分准备。

PROJECT SUMMARY The exposome is defined as the totality of exposures with which the public comes in contact, including toxic chemicals. Exposures to these chemicals represents a huge burden on human health and diseases. It is difficult to perform comprehensive safety assessment of all novel chemicals due to limited time and funds. However, with the vast amount of biological data related to thousands of exposures and their molecular targets, we hypothesize computational methods can be developed to accurately predict the molecular actions and targets of new chemicals. In this proposal, we propose to implement and apply a novel matrix completion algorithm named Coupled Matrix/Tensor-Matrix Completion (CM/TMC) and Coupled Matrix/Tensor-Tensor Completion (CM/TTC) to predict the molecular targets and target tissues of environmental chemical exposures at a large scale. The study proposed will be accomplished through the following specific aims: 1) Apply and optimize the CM/TMC algorithm for exposure-related datasets, comparing results to alternative methods, 2) Optimize the CM/TMC method for exposure target tissue prediction, and 3) develop CM/TTC method on exposure-target predictions, perform experimental validations, and establish a web portal for exposure-target prediction. This study poses the first matrix completion-based method on exposure molecular target predictions and target tissue predictions. The primary goal of the mentored (K99) phase of the award is to provide the candidate with additional training in data science and toxicology for him to acquire scientific independence and successfully accomplish his career objectives. The K99 phase will be conducted at the University of Michigan (UM), under the mentorship of Drs. Maureen Sartor, Justin Colacino, Kayvan Najarian, and Mario Medvedovic, who are experts in the respective fields. An interdisciplinary team of advisors will assist the candidate in his research and career development. After the completion of the K99 phase, the candidate will be well prepared to be an independent investigator.