Edward Pao

High-throughput mapping of kinase-substrate interactions with protein language models and phospho-PCA

This paper develops a framework combining a protein language model and a screening assay to predict kinase-substrate interactions and map the human kinome.

Uncovering the molecular basis of kinase activity and substrate recognition with phospho-PCA

This paper introduces phospho-PCA, a method to quantify kinase-substrate interactions, and applies it to study kinase variants, revealing mutations that affect activity and substrate specificity.

Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Human-in-the-loop labeling of nuclear live-cell movies produced DynamicNuclearNet (over 2,621 divisions); Caliban, a tracker trained on it, beat four published trackers at division detection.

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning

Polaris provides a deep learning-based pipeline for analyzing spatial transcriptomics data, improving spot detection and gene expression quantification.

CellSAM: A Foundation Model for Cell Segmentation

Bounding boxes from a transformer detector let the Segment Anything Model segment cells automatically; one generalist model beats generalist Cellpose and matches inter-annotator agreement across mammalian, yeast and bacterial images.

DeepCell Kiosk: Scaling deep learning-enabled cellular image analysis with Kubernetes

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