Noah F. Greenwald

Lilace improves variant effect estimation in FACS-based DMS by providing uncertainty quantification and better FDR control.

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.

This study uses spatial proteomics to analyze human brain tissues and identifies proteopathies and cell interactions in Alzheimer's disease.

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

The paper constructs a spatiotemporal atlas of the maternal-fetal interface using multiomic data to show how gestational age influences immune and stromal cell interactions and spiral artery remodeling.

The paper introduces DeepLIIF, a deep learning framework that translates IHC images to multiplex IF images and quantifies single-cell IHC markers.

DeepCell Kiosk scales deep learning workflows for large imaging datasets, processing 106 images in ~5.5 hours at a low cost.

SvABA detects structural variants and indels using genome-wide local assembly, showing improved sensitivity and specificity, especially for small variants and complex rearrangements in cancer genomes.