ArticleNature communications2026
Leveraging weighted embedding and Transformer architecture to improve phenotype prediction of complex traits for crops.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Review
- Robustly enhancing crop genomic prediction accuracy through ensemble learning and iterative optimization.Nature communications · 2026Article
- GE-BiFormer: bidirectional cross-attention integration of genomic and Enviromic data for genotype-by-environment prediction in maize.Briefings in bioinformatics · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Leveraging weighted embedding and Transformer architecture to improve phenotype prediction of complex traits for crops.Nature communications · 2026Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Understanding the relationship between genomic variation and phenotype is fundamental to deciphering the genetic architecture underlying complex traits. Yet, existing statistical models struggle to balance massive genomic datasets with biological interpretability. Here, we introduce GP-WAITER, a deep learning framework integrating GWAS-derived SNP weights into a hybrid convolutional neural network and Transformer architecture. By utilizing a weighted embedding mechanism and multi-head self-attention, GP-WAITER effectively captures long-range dependencies across ultra-long genomic sequences. The model consistently outperforms seven state-of-the-art genomic prediction models across six datasets, achieving up to a 77.5% improvement in prediction accuracy, a 78% reduction in mean squared error, and a 1.8-2.4fold increase in computational efficiency. Furthermore, GP-WAITER offers biological transparency by pinpointing key genetic variants driving specific traits. This scalable, interpretable framework provides a powerful tool for precision breeding and the functional interpretation of trait-associated variants.
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What Socratic holds
Registered trials
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 Socratic graph.