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. 

Ämnesord

NATURVETENSKAP  -- Biologi -- Cellbiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Cell Biology (hsv//eng)

Nyckelord

cell protein

protein

Article

cell nucleus inclusion body

classification

competition

computer model

fluorescence imaging

machine learning

multilabel classification

protein function

protein localization

proteomics

single cell analysis

human

Humans

Proteins

Publikations- och innehållstyp

ref (ämneskategori)

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