Shubham Koirala

This paper presents a drug repurposing framework using in vivo transcriptomics to expand indications by matching drug signatures with disease signatures, successfully recovering established uses of semaglutide and proposing new ones.

GEOMeta automates metadata curation for GEO transcriptomic data, generating standardized annotations for 600,000 human bulk RNA-seq samples and benchmarking LLMs for annotation quality.

The paper uses single-cell transcriptomics to identify S100 gene expression patterns and cell-type changes in MODS patients, showing diagnostic potential and therapeutic implications.

The paper develops an in-silico method to select optimal asparaginase enzymes by analyzing homologous proteins and their binding energy and Km values, with asnB from Streptomyces species showing higher binding energy and lower Km than E. coli and Erwinia variants.