Author

Riccardo Conci

Dana-Farber Cancer InstituteORCID 0000-0002-4161-8451Unclaimed 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 Organism:human· 2 Assay:scRNA-seq· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Eliezer M. Van AlleniD· 2 shared Ahmed RomaniD· 1 shared Davide D’AscenzoiD· 1 shared Haitham ElmarakebyiD· 1 shared Lorin A. CrawfordiD· 1 shared Peter S. WinteriD· 1 shared Sebastiano Cultrera di MontesanoiD· 1 shared Shreya JohriiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Eliezer M. Van AllenORCID

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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

Empirical Evaluation of Single-Cell Foundation Models for Predicting Cancer Outcomes

Ahmed Roman, Shreya Johri, Riccardo Conci, Eliezer M. Van Allen, Haitham Elmarakeby

This study evaluates twelve single-cell foundation models for cancer-related tasks and finds they offer limited clinical outcome prediction advantages over simpler models.

bioRxiv · Oct 31, 2025 · PreprintCancer Biology