Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.

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Bibliographic Details
Title: Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.
Authors: Zhao Y; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.; Department of Pathology, Stanford University, Stanford, CA, USA., Chow SSL; Department of Pathology, Stanford University, Stanford, CA, USA.; Department of Mechanical Engineering, University of Washington, Seattle, WA, USA., Yan R; Department of Pathology, Stanford University, Stanford, CA, USA.; Department of Mechanical Engineering, University of Washington, Seattle, WA, USA., Brenes D; Department of Pathology, Stanford University, Stanford, CA, USA., Serafin R; Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL, USA., Almagro-Pérez C; Department of Pathology, Mass General Brigham, Harvard Medical School, Boston, MA, USA.; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA., Song AH; Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Lal P; Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA., Chan E; Stanford Medical Center, Stanford, CA, USA., Downes M; Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada., Baraznenok E; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley, CA, USA., Lopez JS; Wallace H. Coulter Department of Biomedical Engineering, Emory University & Georgia Institute of Technology, Atlanta, GA, USA., Madabhush A; Wallace H. Coulter Department of Biomedical Engineering, Emory University & Georgia Institute of Technology, Atlanta, GA, USA., Mahmood F; Department of Pathology, Mass General Brigham, Harvard Medical School, Boston, MA, USA.; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA.; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.; Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA., True LD; Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA., Liu JTC; Department of Pathology, Stanford University, Stanford, CA, USA.; Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.; Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2026 Jul 11. Date of Electronic Publication: 2026 Jul 11.
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:10.64898/2026.07.07.736891