Improving chemical exposome target prediction by application of Coupled Matrix/Tensor-Matrix/Tensor Completion algorithms
Improving chemical exposome target prediction by application of Coupled Matrix/Tensor-Matrix/Tensor Completion algorithms
批准号:
10734136
负责人:
Kai Wang
金额:
$11.8万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-02 至 2025-07-31
关键词:
AlgorithmsAwardBenchmarkingBioconductorBiologicalChemicalsChemistryCommunitiesComputing MethodologiesCoupledDataData ScienceData SetDatabasesDiseaseDoseDrug DesignDrug TargetingEnvironmental ExposureEnvironmental HealthEnvironmental ScienceExposure toFundingFutureGenomicsGenotype-Tissue Expression ProjectGoalsHealthHumanHuman Cell LineIn VitroMachine LearningMentorsMentorshipMethodsMichiganMolecularMolecular TargetNamesOutcomePerformancePersonsPhasePlayPoisonPoliciesProductionQuantitative Structure-Activity RelationshipReproducibilityResearchResearch PersonnelResourcesRoleScientistStandardizationTargeted ToxinsTestingTimeTissue-Specific Gene ExpressionTissuesToxic effectToxicity TestsToxicogenomicsToxicologyToxinTrainingUniversitiesValidationVisualizationcareercareer developmentcomparativedashboarddata integrationdata portalenvironmental chemicalenvironmental chemical exposuregene environment interactiongene expression databaseimprovedin silicoin vivolarge datasetsnonbinarynovelperformance testspublic health researchresearch and developmentresponsesafety assessmentweb portal
中文摘要
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英文摘要
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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
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]
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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