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

NIH RePORTER · NIH · K99 · $118,114 · view on reporter.nih.gov ↗

Abstract

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.

Key facts

NIH application ID
10897923
Project number
5K99ES034429-02
Recipient
UNIVERSITY OF MICHIGAN AT ANN ARBOR
Principal Investigator
Kai Wang
Activity code
K99
Funding institute
NIH
Fiscal year
2024
Award amount
$118,114
Award type
5
Project period
2023-08-02 → 2025-07-31