Payal Kapur

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets

The paper develops a deep learning model from H&E images to predict T-effector biology and ICI response in ccRCC, showing strong correlation with RNA-based scores and clinical outcomes.

Clear cell renal cell carcinoma consensus transcriptomic programs reveal converging trajectories towards aggressive disease

The paper identifies 17 consensus transcriptomic programs in ccRCC using NMF and links them to cellular sources and clinical outcomes, revealing converging trajectories toward aggressive disease.

A single chromosome 3p break initiates clear cell renal cell carcinoma evolution

This study shows that a single chromosome 3p break initiates ccRCC evolution by creating a fitness bottleneck that selects for recurrent aneuploidies.

Delta Marches: Generative AI based image synthesis to decode disease-driving morphologic transformations.

Delta-Marches uses generative AI to simulate morphological changes between disease classes, identifying subcellular features that drive disease progression in histopathological images.

SCINA: Semi-Supervised Analysis of Single Cells in silico

SCINA is a semi-supervised model for analyzing scRNA-Seq and flow cytometry data, improving accuracy and efficiency over unsupervised methods by leveraging gene signatures.