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.

项目总结

项目成果

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

• DOI: 10.1021/acs.est.4c00458
• 发表时间: 2024-03
• 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