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Prediction of plant phase-separating proteins using positive-unlabeled learning
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Prediction of plant phase-separating proteins using positive-unlabeled learning
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Prediction of plant phase-separating proteins using positive-unlabeled learning
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Journal Article
Prediction of plant phase-separating proteins using positive-unlabeled learning
2026
Overview
Liquid–liquid phase separation regulates biological processes through dynamic condensates. Despite its significance, experimentally validated phase-separating proteins in plants remain limited, complicating predictions. We overcome this gap by applying positive-unlabeled learning, a semi-supervised approach optimized for imbalanced datasets. Leveraging 6,559 reported plant phase-separating proteins from eight species, we train a model integrating sequence-structural features, enabling prediction of 174,656 high-confidence candidates across 14 species. Experimental validation confirms liquid–liquid phase separation in 67.9% of the candidate proteins from
Arabidopsis
, rice, and maize. This positive-unlabeled framework demonstrates robust predictive power while providing open resources to advance plant phase separation research.
Publisher
BioMed Central,Springer Nature B.V,BMC
