Danesh Moradigaravand

This study uses chemical genomics to identify gene-phenotype relationships in Vibrio cholerae by screening 104 stress conditions across 3,026 single-gene mutants, revealing 1,518 significant growth phenotypes including 88 unannotated genes.

This paper characterizes genome-environment interactions in Klebsiella pneumoniae using chemical genomics and machine learning to predict fitness across diverse conditions.

ChemGenXplore provides an interactive web tool for visualizing and analyzing chemical genomic data, enabling users to explore pre-implemented and uploaded datasets with various analytical features.

The paper uses machine learning to predict plasmid permissiveness in wastewater microbiomes from 16S rDNA sequences, showing moderate-to-strong correlations with experimental data.

ChemGAPP is a package for chemical genomics analysis and phenotypic profiling that integrates quality control and provides sub-packages for different screening types.

This paper predicts antibiotic resistance in E. coli from whole genome sequences using machine learning, achieving high accuracy without prior knowledge of resistance mechanisms.