An Integrated Computational Framework for Microbiology, Antimicrobial Susceptibility Testing, and Bioinformatics Research.
We bridge the gap between traditional microbiology and modern computational analysis, with a focus on antimicrobial resistance surveillance and CLSI/EUCAST-compliant susceptibility testing.
Clinical microbiology, antimicrobial resistance (AMR) surveillance, and drug discovery, grounded in CLSI M100 and EUCAST breakpoint standards.
Python and R-based tools for AST data structuring, MDR/XDR/PDR classification, and machine learning applications in AMR data.
Applying scientific knowledge to real-world challenges in pharmaceuticals, quality control, and industrial biotechnology.
AST machine learning toolkit for predicting antimicrobial susceptibility.
View RepositoryCLSI, EUCAST, MPN, drug development & discovery, and AST reference databases.
View RepositoryPython workflow for 2×2, 3×3, and 4×4 AST matrix agreement analysis — a CLSI-compliant method validation tool.
View RepositoryDNA-as-string and Unicode-text-based approaches for data storage.
View Repository
Founder & Principal Investigator
MS student, Department of Microbiology, University of Chittagong, Bangladesh. Works at the intersection of AMR surveillance, clinical microbiology, and computational tools for AST analysis.
01775-510351
01867-771598
University of Chittagong
Chittagong, Bangladesh