Bhaswar Ghosh

The paper investigates how diffusion and protein copy number affect information transmission in yeast signaling systems, finding that active diffusion and higher copy numbers improve accuracy but increase entropy production.

This study uses single-cell transcriptomics and computational methods to identify key proteins in Plasmodium falciparum and predict potential drug molecules.

The paper investigates how gene length affects kinetic parameters like burst size and their impact on mean expression, suggesting implications for drug target selection.

This paper introduces HYDRA, a hybrid deep learning model for generating therapeutic peptides that bind to target receptors, with an application to malaria treatment.

The paper shows that variable switching rates of var genes in Plasmodium falciparum provide an evolutionary advantage by optimizing parasite survival under host immune pressure.

The paper analyzes the trade-off between accuracy and energy cost in a MAPK signaling pathway using non-equilibrium thermodynamics to explain optimal reaction rates.

This paper applies machine learning techniques to classify Plasmodium falciparum life cycle stages using single-cell transcriptomes, achieving improved classification through feature selection and multiple classifiers.