Deep-Learning Tool ScVital Enables Species-Agnostic Integration of Cancer Cell States
ScVital is an algorithm for cross-species integration of single-cell data to identify common cell states and facilitate translation of tumor models towards therapeutic application. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
Authors
- Tuomas Tammela (ORCID: https://orcid.org/0000-0003-3675-6961)
- William D. Tap (ORCID: https://orcid.org/0000-0001-7779-2796)
- Gary Guzman (ORCID: https://orcid.org/0000-0001-8002-9994)
- Jason E. Chan (ORCID: https://orcid.org/0000-0003-0552-8544)
- Carleigh Sussman (ORCID: https://orcid.org/0009-0004-6230-6408)
- Samuel Singer (ORCID: https://orcid.org/0000-0001-9713-8230)
- Doron Betel (ORCID: https://orcid.org/0000-0002-8006-7752)
- Jonathan Rub (ORCID: https://orcid.org/0000-0001-8086-2310)
- Cristina R. Antonescu (ORCID: https://orcid.org/0000-0002-9717-8205)
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Cornell University (US)
- Kettering University (US)
- Weill Cornell Medicine (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1158/0008-5472.can-24-4889
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81