Harsha Gowda

This study evaluates how ensilication preserves transcriptome information during storage using long-read RNA sequencing, finding it retains more transcript data than unprotected RNA.

This study identifies Ephrin-B1-EPHA2 cis-interaction as a driver of oral cancer self-renewal and recurrence, validated through proteomics, biochemical assays, and clinical data.

The paper shows that ccdA antitoxin in E. coli is highly sensitive to mutations, including synonymous ones, due to codon usage and operonic context effects on translation efficiency.

This study uses deep learning to classify 37 cancer types from TCGA data, identifying cancer-specific gene expression signatures with >97% accuracy.

This study identifies potential biomarkers for intracranial aneurysm subtypes through proteomic analysis of tissue and serum samples.

The paper shows that synonymous mutations can cause loss of function by altering RNA structure and gene expression ratios, validated in multiple operons.