Author

Sebastiano Cultrera di Montesano

Broad InstituteORCID 0000-0001-6249-0832Unclaimed 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

Davide D’AscenzoiD· 2 shared Lorin A. CrawfordiD· 2 shared Peter S. WinteriD· 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

Davide D’AscenzoORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 1, 2026

Single-cell perturbation prediction near experimental reproducibility with GENESIS

Riccardo Conci, Davide D’Ascenzo, Sebastiano Cultrera di Montesano…·William E. Lotter

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

DOI 10.64898/2026.10.30.763081

humanscRNA-seq

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Cell BiologybioRxivPreprintApr 23, 2025

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

Sebastiano Cultrera di Montesano, Davide D’Ascenzo, Srivatsan Raghavan…·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.

3 citations

scRNA-seqdeep learning

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