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

通过应用耦合矩阵/张量矩阵/张量完成算法改进化学暴露组目标预测

• 批准号: 10734136
• 负责人: Kai Wang
• 金额: $ 11.8万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-02 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734136
• 关键词: Algorithms; Award; Benchmarking; Bioconductor; Biological; Chemicals; Chemistry; Communities; Computing Methodologies; Coupled; Data; Data Science; Data Set; Databases; Disease; Dose; Drug Design; Drug Targeting; Environmental Exposure; Environmental Health; Environmental Science; Exposure to; Funding; Future; Genomics; Genotype-Tissue Expression Project; Goals; Health; Human; Human Cell Line; In Vitro; Machine Learning; Mentors; Mentorship; Methods; Michigan; Molecular; Molecular Target; Names; Outcome; Performance; Persons; Phase; Play; Poison; Policies; Production; Quantitative Structure-Activity Relationship; Reproducibility; Research; Research Personnel; Resources; Role; Scientist; Standardization; Targeted Toxins; Testing; Time; Tissue-Specific Gene Expression; Tissues; Toxic effect; Toxicity Tests; Toxicogenomics; Toxicology; Toxin; Training; Universities; Validation; Visualization; career; career development; comparative; dashboard; data integration; data portal; environmental chemical; environmental chemical exposure; gene environment interaction; gene expression database; improved; in silico; in vivo; large datasets; nonbinary; novel; performance tests; public health research; research and development; response; safety assessment; web portal

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.

项目概要 暴露组被定义为公众接触到的暴露的总和，包括 有毒化学品。接触这些化学物质对人类健康和疾病造成巨大负担。 由于时间和条件有限，很难对所有新型化学品进行全面的安全评估 资金。然而，由于存在与数千次暴露及其相关的大量生物数据， 分子目标，我们假设可以开发计算方法来准确预测 新化学品的分子作用和目标。在本提案中，我们建议实施并应用 名为耦合矩阵/张量矩阵完成 (CM/TMC) 的新型矩阵完成算法 和耦合矩阵/张量-张量完成 (CM/TTC) 来预测分子目标和 大规模环境化学暴露的目标组织。拟议的研究将是 通过以下具体目标来实现： 1）应用和优化 CM/TMC 算法 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 暴露目标组织预测，3) 开发暴露目标预测的 CM/TTC 方法， 进行实验验证，并建立一个用于暴露目标预测的门户网站。这项研究 提出了第一个基于矩阵补全的曝光分子目标预测和目标方法 组织预测。该奖项的指导（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 成功实现他的职业目标。 K99阶段将在大学进行 密歇根大学（UM），在博士的指导下。莫林·萨托、贾斯汀·科拉西诺、凯万·纳贾里安和 马里奥·梅德韦多维奇（Mario Medvedovic）是各自领域的专家。跨学科的顾问团队将 协助候选人的研究和职业发展。 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。

项目成果

Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.

使用耦合矩阵-矩阵完成来预测环境化学品的基因靶标。

• DOI: 10.1021/acs.est.4c00458
• 发表时间: 2024-03-19
• 期刊: Environmental science & technology
• 影响因子: 11.4
• 作者: Kai Wang;Nicole Kim;M. Bagherian;Kai Li;Elysia Chou;Justin A. Colacino;Dana C. Dolinoy;Maureen A. Sartor
• 通讯作者: Maureen A. Sartor