Author

Davide D’Ascenzo

Politecnico di TorinoORCID 0000-0002-0607-8488Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:scRNA-seq· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Organism:human· 1 Research Focus:perturbation response prediction· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Lorin A. CrawfordiD· 2 shared Peter S. WinteriD· 2 shared Sebastiano Cultrera di MontesanoiD· 2 shared Srivatsan RaghavaniD· 2 shared Ava P. AminiiD· 1 shared Eliezer M. Van AlleniD· 1 shared Riccardo ConciiD· 1 shared William E. LotteriD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Lorin A. CrawfordORCID

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2 papers

Recent·Most cited

Single-cell perturbation prediction near experimental reproducibility with GENESIS

Riccardo Conci, Davide D’Ascenzo, Sebastiano Cultrera di Montesano, Srivatsan Raghavan, Peter S. Winter, Eliezer M. Van Allen, Lorin A. Crawford, William E. Lotter

GENESIS predicts single-cell perturbation responses from experimental conditions, outperforming existing models and approaching experimental reproducibility.

bioRxiv · Nov 1, 2026 · PreprintBioinformatics

Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models

Sebastiano Cultrera di Montesano, Davide D’Ascenzo, Srivatsan Raghavan, Ava P. Amini, Peter S. Winter, Lorin A. Crawford

This paper introduces a hierarchical cross-entropy loss to improve single-cell annotation models by aligning them with biological hierarchies, achieving better out-of-distribution performance.

bioRxiv · Apr 23, 2025 · PreprintCell Biology