Author

S. Ciccolella

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:phylogenetics / evolution· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:clustering· 1 Organism:human· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

G. Della Vedova· 2 shared M. Patterson· 2 shared P. Bonizzoni· 2 shared I. Hajirasouliha· 1 shared M. Soto Gomez· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersCancer Biology

G. Della Vedova

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2 papers

Recent·Most cited

Cancer BiologybioRxivMay 14, 2021

Effective clustering for single cell sequencing cancer data

S. Ciccolella, M. Patterson, P. Bonizzoni, G. Della Vedova

This paper introduces a clustering method for single-cell sequencing cancer data, improving runtime and scalability while maintaining accuracy in phylogenetic inference.

DOI 10.1109/jbhi.2021.3081380

humanscRNA-seq

𝕏 PostBluesky

Cancer BiologybioRxivSep 4, 2018

gpps: An ILP-based approach for inferring cancer progression with mutation losses from single cell data

S. Ciccolella, M. Soto Gomez, M. Patterson…·P. Bonizzoni

gpps infers cancer progression from single-cell data by allowing mutation losses, improving upon traditional methods that assume mutations are irreversible.

DOI 10.1186/s12859-020-03736-7

whole-genome sequencingdeep learning

𝕏 PostBluesky