Akanksha Rajput

This paper uses transcriptomics and machine learning to reconstruct the transcriptional regulatory network of L. reuteri, identifying key regulatory signals and metabolic pathways.

This study performs a pangenome analysis of the Lactobacillaceae family, revealing correlations between pangenome openness, transposon abundance, and species-specific genomic features.

This study uses transcriptomic data to construct a regulatory network of Pseudomonas aeruginosa, revealing roles of efflux pumps and media composition in antibiotic responses.

The paper uses machine learning to analyze Pseudomonas aeruginosa transcriptomes and identifies gene sets regulated by known transcriptional factors, revealing insights into biosynthetic clusters and metabolic regulation.

This study compares microarray and RNA-seq expression profiling of Nipah virus to identify differentially expressed genes and potential drug targets.