Marcin Możejko

The paper introduces FACTMx, a variational framework that jointly models structured and simple patient-level data to learn interpretable representations, showing improved performance in simulations and applications to cancer and cardiovascular cohorts.

This study identifies distinct pneumonia endotypes based on pathogen-specific host responses in the lung, linking them to clinical outcomes and suggesting personalized treatment strategies.

This paper introduces GUT-FORMer, a transformer-based autoencoder for gut microbiome analysis, achieving accurate disease classification and host trait prediction.

Cellohood identifies cellular neighborhoods in spatial transcriptomics data, revealing clinically relevant patterns across multiple granularity levels.

The paper presents two workflows for analyzing multiplexed imaging data from cancer tissues, applied to over 10,000 samples from 2500 patients, enabling large-scale tumor microenvironment studies.

This paper introduces HydrAMP, a deep generative model for creating potent antimicrobial peptides, which outperforms existing methods and validates nine new peptides in lab tests.

The paper introduces a Bayesian deep learning framework for histopathological image classification, achieving high accuracy and providing uncertainty measures to improve active learning efficiency.