Abhibhav Sharma

This study integrates DNA methylation, gene expression, and metabolic profiles to identify molecular pathways and signatures associated with aggressive prostate cancer and disease recurrence.

The paper identifies molecular signatures in prostate cancer that link inflammatory chemokine expression to metabolic changes and relapse risk.

The paper identifies club-like cells in the prostate tumor microenvironment that contribute to androgen deprivation therapy resistance through immune suppression.

The paper uses molecular dynamics simulation and machine learning to analyze disease-associated mutations in TDP-43 and identifies structural features linked to protein stability and pathogenesis.

The paper uses machine learning to identify novel gene signatures and predict Alzheimer's disease in specific brain regions with high accuracy.

AE-LGBM predicts protein-protein interactions using an ensemble of autoencoder and LightGBM, achieving high accuracy across multiple organisms and PPI networks.