Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

RGPA-GCN: Graph convolutional networks for rice gene-phenotype association prediction.

Plant biology (Stuttgart, Germany) - 1 Aug 2026

Luo J, Wang X, Li X, Zhou Q, Xiang Y, Yue Z, Gao Y

Abstract excerpt

Rice plays a pivotal role as a vital food source for human consumption. Identifying gene-phenotype associations (GPAs) can significantly enhance the tolerance of rice to environmental stress and its overall yield. Nevertheless, the experimental process of discovering GPAs is not only consume a lot of resources but also time-consuming. The computational screening for GPAs has emerged as an essential tool to...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Computational Biology
Share to Genes, Plant
Share to Genetic Association Studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 41865388
DOI: 10.1111/plb.70212
PMCID: PMC13358646

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

RGPA-GCN: Graph convolutional networks for rice gene-phenotype association prediction.PMID 41865388Read ↗

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations.Semantic proximity Read ↗

PRSNet-2: End-to-end genotype-to-phenotype prediction via hierarchical graph neural networksSemantic proximity Read ↗

Crop-GPA: an integrated platform of crop gene-phenotype associations.Semantic proximity Read ↗

HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations.Semantic proximity Read ↗

Supervised-learning is an accurate method for network-based gene classificationSemantic proximity Read ↗

SMG: self-supervised masked graph learning for cancer gene identification.Semantic proximity Read ↗

OneGenome-Rice (OGR): A genomic foundation model for riceSemantic proximity Read ↗

Network-guided supervised learning on gene expression using a graph convolutional neural networkSemantic proximity Read ↗

G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers.Semantic proximity Read ↗

Candidate gene prioritization using graph embeddingSemantic proximity Read ↗

Leveraging weighted embedding and Transformer architecture to improve phenotype prediction of complex traits for crops.Semantic proximity Read ↗

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.