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

Award Number: K99ES034429
ORGANIZATION: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $118,114 )
2024	2024	REGENTS OF THE UNIVERSITY OF MICHIGAN	1109 GEDDES AVE, SUITE 3300	ANN ARBOR	MI	48109	WASHTENAW	USA	Environmental Health	000	2	7/15/2024	NON-COMPETING CONTINUATION	$118,114
														Subtotal = $118,114

Issue Date FY: 2023 ( Subtotal = $117,971 )
2023	2023	REGENTS OF THE UNIVERSITY OF MICHIGAN	1109 GEDDES AVE, SUITE 3300	ANN ARBOR	MI	48109	WASHTENAW	USA	Environmental Health	000	1	8/2/2023	NEW	$117,971
														Subtotal = $117,971

Grand Total All Awards = $236,085

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

Award Number: K99ES034429

ORGANIZATION: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

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