Gaurav Shankar Pandey

This study uses machine learning to predict cell division from ERK and Akt activity dynamics in single cells, finding ERK dynamics more predictive than Akt.

KinAce integrates kinase-substrate interactions from multiple databases to enable visual exploration in systems contexts.

This study evaluates AI-generated kinase structures to explore underrepresented conformational states, showing that reducing MSA depth in AlphaFold2 can predict novel conformations for 398 kinases.

The paper introduces Ensemble Integration (EI), a late integration method for multimodal biomedical data, achieving more accurate predictions than individual modalities and early integration approaches.

The paper uses single-cell imaging and machine learning to predict cell division from ERK and Akt activity dynamics in MCF10A cells, finding ERK alone predicts division with AUC=0.74.

This paper develops an interpretable decoding model using functional connectivity to classify chronic marijuana users and non-users with high accuracy.

The paper develops a machine learning classifier for asthma using nasal RNA sequencing data, achieving high accuracy across multiple test sets.