Neural Networks for Predicting and Classifying Antimicrobial Resistance Sequences in Porphyromonas gingivalis.

Journal: International dental journal
Published Date: Jul 5, 2025

Abstract

INTRODUCTION AND OBJECTIVE: Porphyromonas gingivalis is a key pathogen associated with periodontal disease linked to various systemic conditions. Accurate identification of P. gingivalis proteins is essential for understanding its pathogenicity and developing targeted interventions. Recent advances in whole-genome sequencing of P. gingivalis have enhanced the detection and classification of antimicrobial resistance (AMR) determinants, aiding in the early identification of resistance trends and improving patient care. In this study, we developed a deep learning approach using convolutional neural networks (CNNs) to classify P. gingivalis proteins based on their amino acid sequences.

Authors

  • Pradeep Kumar Yadalam
    Department of Periodontics, Saveetha Dental College, Saveetha Institute of Medical and Technology Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
  • Raghavendra Vamsi Anegundi
    Department of Periodontics, Saveetha Dental College, Saveetha Institute of Medical and Technology Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
  • Prabhu Manickam Natarajan
    Department of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry, Ajman University, Ajman, United Arab Emirates.
  • Carlos M Ardila
    Basic Sciences Department, Faculty of Dentistry, Universidad de Antioquia, Medellin, Colombia.

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