Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition (CROSBI ID 317525)
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Podaci o odgovornosti
Le, Trang ; Winsnes, Casper F. ; Axelsson, Ulrika ; Xu, Hao ; Mohanakrishnan Kaimal, Jayasankar ; Mahdessian, Diana ; Dai, Shubin ; Makarov, Ilya S. ; Ostankovich, Vladislav ; Xu, Yang ; Benhamou, Eric ; Henkel, Christof ; Solovyev, Roman A. ; Banić, Nikola ; Bošnjak, Vito ; Bošnjak, Ana ; Miličević, Andrija ; Ouyang, Wei ; Lundberg, Emma
engleski
Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition
While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas – Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.
fluorescence imaging ; human protein atlas ; single-cell classification ; single-cell features ; cellular dynamics
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