Evidence map›Paper›PMID 41987292›Full record

ArticleGenome biology2026

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ran Fu *State Key Laboratory of Maize Bio-Breeding, Sanya Institute of China Agricultural University, Sanya, China.
Yisu Tian *State Key Laboratory of Maize Bio-Breeding, Sanya Institute of China Agricultural University, Sanya, China.
Hui RenState Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Anwen ZhaoNational Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China.
Yuxuan LouNational Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China.
Shiya MaoNational Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China.
Jing YangNational Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China.
Shan JiangNational Nanfan Research Institute, Chinese Academy Of Agricultural Sciences, Sanya, China.
Xi HuangState Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Xiangfeng WangState Key Laboratory of Maize Bio-Breeding, Sanya Institute of China Agricultural University, Sanya, China. xwang@cau.edu.cn.

Funding

National Key Research and Development Program of China 2024YFD1201300Pinduoduo-China Agricultural University Research Fund PC2024A01003Postdoctoral Fellowship Program of CPSF GZC20233044
6 · The paper itself

Abstract

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.

Indexed as

Plant ProteinsArabidopsisOryzaPhase SeparationPrediction AlgorithmsZea maysPlant ProteinsMultimodal featuresPlant phase-separating proteinsPositive-unlabeled learningSemi-supervised learning framework

Identifiers

PMID41987292
PMCPMC13192194

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.