Matei Ionita

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

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

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

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

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

BTS identifies causal variants and their functional contexts using GWAS data and functional genomic annotations, showing improved efficiency and biological insights.