Robert F. Murphy

This paper introduces RIBCA, a tool for automated cell type annotation in multiplexed tissue images, capable of annotating over 3 million cells across 40 human tissues without manual intervention.

CytoSpatio models spatial relationships between cell types across tissues using multirange, multitype point processes and generates synthetic tissue structures.

3DCellComposer uses 2D segmentation models to generate accurate 3D cell segmentations without requiring additional training or human annotations.

The paper investigates how Big1 regulates cell size by controlling basal body number through cell cycle progression in Tetrahymena.

The paper improves protein interaction prediction by addressing incomplete features and proteome coverage, identifying cell line-specific complexes.

This paper introduces a method to predict full protein images from a minimal marker subset, outperforming previous approaches and enabling prediction of a larger set of markers than previously measurable.

The paper evaluates cell segmentation methods using automated metrics instead of human reference segmentations, finding two deep learning methods perform best with improved results via postprocessing.

The paper improves GAN-based deep learning models for cell organization and introduces evaluation criteria to assess synthetic cell images' accuracy.

The paper compares different computational methods for simulating cellular biochemistry and identifies discrepancies in results, emphasizing the need for critical evaluation of models.

The paper uses fluorescence in situ hybridization and point process models to infer the sequence of vRNP associations during influenza A virus assembly.

The paper develops a generative model to track cell organization changes during PC12 differentiation using static images and shape analysis.