Rahul M. Dhodapkar

The paper shows that isotonic tartrazine improves OCT penetration without increasing ocular toxicity compared to hyperosmolar concentrations.

This paper scales large language models for single-cell analysis, improving predictive and generative capabilities and enabling biological discovery through virtual screening.

CINEMA-OT identifies causal effects of single-cell perturbations by separating confounding variation, outperforming existing methods on simulated and real datasets.

A masked-prediction model pretrained on 6,700 hours of fMRI that fine-tunes to predict clinical variables, forecasts future brain states, and recovers functional networks without supervision.

Cell2Sentence converts single-cell gene expression profiles into text-like cell sentences so standard large language models like GPT-2 can be fine-tuned for cell generation and cell-type annotation.

SpikeGPT2, a deep generative model of SARS-CoV-2 spike protein sequences, predicts future mutations with high accuracy and identifies variants that may increase transmissibility and immune evasion.

The paper converts gene expression data into sentence-like structures to apply natural language processing techniques in single-cell transcriptomics.

A topological machine learning approach applied to single-nucleus RNA sequencing identifies shared glial activation states in AMD, AD, and MS.