PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.

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Bibliographic Details
Title: PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns.
Authors: Sankaranarayanan A; Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA., Zhao C; Department of Biochemistry, University of Washington, Seattle, WA 98195, USA., Hernandez MG; Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA., Clemens EA; Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA., Smythe KS; Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA., Kazerouni AS; Department of Radiology, University of Washington, Seattle, WA 98195, USA., Carr LL; Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA., Li CI; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington., Partridge SC; Department of Radiology, University of Washington, Seattle, WA 98195, USA.; Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.; Department of Bioengineering, University of Washington, Seattle, WA 98195, USA., Vinayak S; Division of Hematology/Oncology, University of Washington, Seattle, WA 98195, USA.; Division of Medical Oncology, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA., Mittal S; Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA.; Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2026 Jun 16. Date of Electronic Publication: 2026 Jun 16.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.64898/2026.06.11.731702