AMR Curator-An Interactive Platform for Analysing AMR Literature Using NLP
Umesh Rani1, Naval Singh1, Deepshikha Kaushik1★★ Corresponding author
- 1Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, INDIA.
CORRESPONDENCE
Deepshikha Kaushik
Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, INDIA.
Received: 24-12-2025; Revised: 09-02-2026; Accepted: 18-04-2026.
Volume 15, Issue 1 · pp. 143–149 · PUBLISHED 2026 · DOI: 10.5530/ajbls.20260077
ABSTRACT
Background: The rising issue of Antimicrobial Resistance (AMR) poses a major threat to global health. Objectives: This study introduces a Streamlit application that helps analyze and visualize trends in AMR literature using natural language processing and interactive data visualization. Materials and Methods: We curated a PubMed dataset comprising abstracts published from 2015 to 2025 and used advanced techniques such as topic modeling, named entity recognition, and keyword analysis. We processed 154608 AMR-related abstracts, identified five main research themes by topic modelling, categorized biomedically relevant entities into key pathogens, antimicrobial agents, and geographical entities using NER. Results: Our user-friendly interface represents literature-based AMR analysis tools that combines multiple analytical methods into a single platform. The platform is freely accessible at [https://d-k-02-amr-dashboard-app-qdprm0. streamlit.app/]. Conclusion: This tool offers researchers, epidemiologists, and policymakers an interactive resource for tracking research trends and making informed decisions in antimicrobial stewardship.
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Rani, U., Singh, N., & Kaushik, D. (2026). AMR Curator-An Interactive Platform for Analysing AMR Literature Using NLP. Asian Journal of Biological and Life sciences, 15(1), 143–149. https://doi.org/10.5530/ajbls.20260077