Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition.

ABSTRACT: 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

SUBMITTER: Le T 

PROVIDER: S-EPMC9550622 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature