Abstract
Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.
Original language | English (US) |
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Article number | 409 |
Journal | Communications Biology |
Volume | 7 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2024 |
ASJC Scopus subject areas
- Medicine (miscellaneous)
- General Biochemistry, Genetics and Molecular Biology
- General Agricultural and Biological Sciences