Michelle L. McKeague

Limits of marker information transfer]{Limits of marker information transfer in cross-panel cytometry imputation

This study evaluates the limits of marker information transfer in cross-panel cytometry imputation, finding that imputation preserves broad lineage classification but not fine-phenotype details, and that patient matching improves cross-platform distribution recovery.

CytoGate-Bench: an LLM benchmark for cross-panel cell gating in cytometry

CytoGate-Bench evaluates LLMs for cross-panel cell gating in cytometry by reformulating manual gating as a zero-shot task using expert-annotated data.

Deep learning representations of human Immune Health for precision immunology

The paper develops MAESTRO, a self-supervised deep learning framework to transform immune profiles into embeddings capturing immune health, enabling clinical prediction and precision immunotherapy.

Cleanet: robust doublet detection in cytometry data based on protein expression patterns

Cleanet detects and classifies doublets in cytometry data using protein expression patterns, improving accuracy over traditional bivariate gating methods.

Cellular and transcriptional profiles of peripheral blood mononuclear cells pre-vaccination predict immune response to preventative MUC1 vaccine

The paper uses single-cell RNA sequencing to identify pre-vaccination immune cell types, gene expression patterns, and pathways that predict immune response to a preventative MUC1 vaccine.

Large-scale evaluation of proteomic and polygenic risk scores reveals complementary contributions to incident disease prediction

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation

This paper introduces UNITO, a deep learning framework for automated cytometric gating that matches human performance and provides interpretable results without requiring manual input.

Cytometry Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper

The paper introduces cyMAE, a masked autoencoder for automated immunophenotyping, showing it improves accuracy and interpretability over manual methods in cytometry data analysis.