Author

Pushpak Pati

IBM Research - ZurichORCID 0000-0003-2174-4255Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell-cell communication· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Assay:Hi-C· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Adriano Luca MartinelliiD· 3 shared Marianna RapsomanikiiD· 3 shared Federico GossiiD· 2 shared Éva CompératiD· 1 shared Francesco BonolloiD· 1 shared Henry Valeyre· 1 shared Marianna Kruithof‐de JulioiD· 1 shared Martin SpahniD· 1 shared

Activity by Year

2022-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersBioinformatics

Adriano Luca MartinelliORCID

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3 papers

Recent·Most cited

Multiplexed tumor profiling with generative AI accelerates histopathology workflows and improves clinical predictions

Pushpak Pati, Sofia Karkampouna, Francesco Bonollo, Éva Compérat, Martina Radić, Martin Spahn, Adriano Luca Martinelli, Martin Wartenberg, Marianna Kruithof‐de Julio, Marianna Rapsomaniki

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

bioRxiv · Nov 29, 2023 · PreprintBioinformatics

MatchCLOT: Single-Cell Modality Matching with Contrastive Learning and Optimal Transport

Federico Gossi, Pushpak Pati, Adriano Luca Martinelli, Marianna Rapsomaniki

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.

bioRxiv · Nov 16, 2022 · PreprintBioinformatics

ChromFormer: A transformer-based model for 3Dgenome structure prediction

Henry Valeyre, Pushpak Pati, Federico Gossi, Vignesh Ram Somnath, Adriano Luca Martinelli, Marianna Rapsomaniki

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.

bioRxiv · Nov 15, 2022 · PreprintBioinformatics