Neal G. Ravindra

X-Cell predicts causal perturbation responses across diverse cellular contexts using a diffusion language model trained on a large CRISPRi Perturb-seq dataset.

DINOcell predicts cellular responses to perturbations using self-distillation in single-cell transcriptomics, outperforming baselines for single and combination gene effects.

This study uses longitudinal single-nucleus RNA sequencing to analyze transcriptional dynamics in SCA1 cerebellum, identifying early cellular changes and applying deep learning to predict disease state.

This study uses single-cell RNA sequencing to identify molecular markers of forelimb and hindlimb projecting corticospinal neurons, revealing subtype-specific gene expression patterns.

The paper presents a drug-target-mechanism graph to identify synergistic drug combinations for SARS-CoV-2 treatment, validated through experimental testing.

Time-course single-cell RNA sequencing of infected human bronchial epithelial cultures reports ciliated cells as a major target, with tropism later widening to basal and club cells.