Source: UNIV OF MARYLAND submitted to
MACHINE LEARNING-BASED APPROACH TO CHARACTERIZE GENETIC SIGNATURES OF VIBRIO PARAHAEMOLYTICUS
Sponsoring Institution
State Agricultural Experiment Station
Project Status
ACTIVE
Funding Source
STATE
Reporting Frequency
Annual
Accession No.
1033728
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Feb 3, 2025
Project End Date
Jun 30, 2026
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
UNIV OF MARYLAND
(N/A)
COLLEGE PARK,MD 20742
Performing Department
Nutrition and Food Science
Non Technical Summary
Vibrio parahaemolyticus is one among the leading cause of illnesses and outbreaks associated with seafood consumption worldwide. Distinguishing its survival, virulence, and antibiotic resistance characteristics through the farm-to-table supply chain is crucial in assessing and managing risks posed by V. parahaemolyticus. The advances in pangenome analysis and the tremendous applications of machine learning in food safety provide an unprecedented opportunity to reveal the differences between V. parahaemolyticus with various originations from the genetic level using an integrative computational method. However, varying results could be yielded by different pangenome pipelines due to differences in back-end algorithms and parameters, which greatly affect the downstream analysis. Thus, this study aims to create a standardized workflow for the machine learning-based approach to analyze the pangenome of V. parahaemolyticus and apply the developed method to characterize the genetic signatures of V. parahaemolyticus isolated from environmental, seafood, and clinical samples. Results obtained from this study will provide useful implications for improving ways to manage risks associated with V. parahaemolyticus.
Animal Health Component
80%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
7124010104040%
7124010208030%
7124010209030%
Knowledge Area
712 - Protect Food from Contamination by Pathogenic Microorganisms, Parasites, and Naturally Occurring Toxins;

Subject Of Investigation
4010 - Bacteria;

Field Of Science
2090 - Statistics, econometrics, and biometrics; 1040 - Molecular biology; 2080 - Mathematics and computer sciences;
Goals / Objectives
The overall goal of this project is to characterize the genetic profiles of Vibrio parahaemolyticus using a standardized pangenome and machine learning-based approach.
Project Methods
We have two components to successfully achieve our objectives as proposed in this project. The first component involves creating a standardized workflow for the pangenome-random forest analysis for V. parahaemolyticus. The second component of the proposed research comprises using the developed workflow to characterize the genetic profiles of V. parahaemolyticus and identify the key genes associated with different functional categories.