Prediction of plant phase-separating proteins using positive-unlabeled learning
Ran Fu, Yisu Tian, Hui Ren, Anwen Zhao, Yuxuan Lou, Shiya Mao, Jing Yang, Shan Jiang, Xi Huang, Xiangfeng Wang · 2026
Read Prediction of plant phase-separating proteins using positive-unlabeled learning by Ran Fu, Yisu Tian, Hui Ren, Anwen Zhao, Yuxuan Lou, Shiya Mao, Jing Yang, Shan Jiang, Xi Huang, Xiangfeng Wang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13192194.txt.
Description
A peer-reviewed research article from Europe PMC (2026). 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.
Catalog record
| CREATOR | Ran Fu, Yisu Tian, Hui Ren, Anwen Zhao, Yuxuan Lou, Shiya Mao, Jing Yang, Shan Jiang, Xi Huang, Xiangfeng Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |