The Molecular Genetics and Bioinformatics Lab provides curated datasets and computational tools for microbial genetics research. The portal supports plate-image collection, pathogen classification, sequence analysis, and reproducible bioinformatics workflows for students, microbiologists, and clinical research teams.
Crowdsource and annotate microbiological plate images. Labels cover 20 common human pathogens, including sample medium, source, resistance profile, and notes. Used to train model checkpoints.
Real-time computer vision classifier utilizing YOLO. Detects, counts, and classifies bacterial colonies across 24 distinct pathogen families from agar plate image uploads.
A tool to predict virulence factors in parasitic organisms using pre-trained machine learning models.
A comprehensive web server for predicting pathogen virulence using protein language models and physicochemical properties.
AI-driven cattle breed identification and continuous ancestry composition estimation for indigenous Indian cattle breeds using multi-model machine learning on genomic SNP data.
Scientist-E
National Institute of Animal Biotechnology (NIAB), Hyderabad
Research Interest: Bioinformatics approaches to decipher the Molecular Genetics of Bovine Disease Pathogenesis
Research Interest: Integrating computational and experimental approaches to identify and validate druggable targets and discover lead compounds against bovine mastitis-causing pathogens
Research Interest: Interrogating the vaginal and uterine microbiomes of non-obstructive (Repeat Breeder) cyclic Gir cows to understand microbial aspects of infertility
Research Interest: Understanding the molecular mechanisms that govern cellular resilience and adaptation to heat stress in indigenous and crossbred cattle.
Research Interest: Computer-Vision Based Framework for Antimicrobial Susceptibility Testing