Alex Kong

This study analyzes multi-omic data from gliomas to identify therapeutic escape mechanisms and TME evolution, finding that N-glycosylation classifies tumor grade and immune transcriptome predicts GBM survival.

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

This study identifies hypoxia as a key driver of immune dysfunction in TB granulomas, correlating with bacterial persistence.

The paper uses spatial imaging and computational analysis to identify features in metastatic tumors that predict response to immune checkpoint inhibition in TNBC patients.

QUICHE identifies low-prevalence cellular niches in spatial datasets, outperforming existing methods, and reveals structural anti-tumor immune responses in TNBC.

The paper introduces Pixie, a pipeline for robust pixel-level clustering in multiplexed imaging data to improve phenotyping and annotation across biological contexts.

The paper introduces Mesmer, a deep learning algorithm for cell segmentation in tissue images, achieving human-level performance and enabling automated feature extraction.