Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas hydrophila

Authors

• Harun Pirim, North Dakota State University
• Zaidur Rahman, University of Arkansas
• Saviz Saei, Mississippi State University
• Sangam Gyawali, Warner Bros. Discovery
• Mohammad Marufuzzaman, Mississippi State University
• Nazanin Tajik, Mississippi State University
• Hasan Tekedar, Mississippi State University

ORCID

Saei: https://orcid.org/0000-0001-8125-435X

MSU Affiliation

James Worth Bagley College of Engineering; Department of Industrial and Systems Engineering

Creation Date

2026-06-01

Abstract

Aeromonas hydrophila, antibiotic resistant gram negative bacteria, is a major fish pathogen. Moreover, A. hydrophila is considered to cause 13% of gastroenteritis cases in the United States. Therefore, it is important to identify groups of proteins that are effective in antibiotic resistance and causing mortality in aquaculture. We train machine learning models on existing A. hydrophila genomes to predict functions of 83 carefully filtered hypothetical proteins. Network analysis is conducted to cluster these proteins based on their similarities. Both ML and network analysis inform about possible roles of these proteins in vaccine candidacy and fish mortality.

Publication Date

7-29-2025

Publication Title

bioRxiv

Publisher

bioRxiv

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights

© 2025 The Author(s)

Recommended Citation

Pirim, H., Rahman, Z., Saei, S., Gyawali, S., Marufuzzaman, M., Tajik, N., & Tekedar, H. (2025). Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas hydrophila. Bioinformatics. https://doi.org/10.1101/2025.07.22.666223