Marianna Rapsomaniki

The paper uses spatial single-cell proteomics to map multicellular niches in primary prostate cancer, identifying prognostic cell types and niches beyond Gleason grading.

This study develops a platform to analyze spatial heterogeneity in colorectal tumor organoids using spatial transcriptomics and validates it against single-cell and tumor spatial data.

The paper identifies chemoresistant cell populations in pediatric AML that improve risk prediction and reveal therapeutic targets for high-risk patients.

This paper uses conditional optimal transport to model CAR T cell responses at the single-cell level and predict gene expression for unseen CAR variants.

This paper introduces VirtualMultiplexer, a generative AI tool that translates H&E images to IHC images, improving clinical predictions and accelerating histopathology workflows.

MatchCLOT improves modality matching in single-cell data by combining contrastive learning and optimal transport, achieving state-of-the-art results on a benchmark challenge.

ChromFormer predicts 3D chromatin structures from Hi-C data using a transformer-based model, outperforming existing methods on synthetic data and matching results on experimental data.

The paper presents a stochastic hybrid model of DNA replication in fission yeast that incorporates protein mobility dynamics and experimental data to simulate full-genome duplication and replication timing.

This paper introduces TECH-3D, a transfer learning method for 3D chromatin structure prediction from Hi-C data, outperforming previous methods without assuming contact frequency equals Euclidean distance.

The paper presents a stochastic hybrid model of DNA re-replication in fission yeast, showing cell-to-cell heterogeneity and genome plasticity through simulations and experimental validation.