Patryk Orzechowski

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.

This paper introduces QOT, a method for efficiently computing sample-level distance matrices from single-cell omics data, showing improved accuracy and robustness over existing OT-based approaches.

The paper introduces a Bioconductor package called runibic that provides a parallel version of the UniBic biclustering algorithm for gene expression data, improving speed and memory efficiency.

The paper learns patient trajectories from EHR data using deep learning methods and compares them to traditional machine learning approaches.