ArticlePlant communications2025
Cropformer: An interpretable deep learning framework for crop genomic prediction.
Article in Plant communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.The New phytologist · 2026Article
- Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- Machine learning to predict genotypes and genotype-environment interaction associated with complex traits for genomic selection.Plant phenomics (Washington, D.C.) · 2026Article
- Genomic prediction vs. gene-based crop models: a case study on rice trait prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Robustly enhancing crop genomic prediction accuracy through ensemble learning and iterative optimization.Nature communications · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Gsformer: a dual-architecture deep learning framework with CNN-self-attention and sparse-attention for genomic selection.Genetics, selection, evolution : GSE · 2026Article
- DNAwhisper: An Integrated Deep Learning Pyramidal Framework for Multi-Trait Genomic Prediction and Adaptive Marker Prioritisation.Plant biotechnology journal · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice.The plant genome · 2026Article
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- KineticGP: A computational framework for genomic prediction of leaf photosynthetic traits.Plant communications · 2026Article
- Beyond Data: Artificial intelligence, knowledge graphs, and the next revolution in wheat breeding.Plant communications · 2026Review
- PCLPred: identifying plant chloride transport-related proteins using reduced amino acid alphabets and N-peptide composition.Amino acids · 2026Article
- CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Briefings in bioinformatics · 2026Article
- Leveraging weighted embedding and Transformer architecture to improve phenotype prediction of complex traits for crops.Nature communications · 2026Article
- MeNet: A mixed-effect deep neural network for multi-environment genomic prediction of agronomic traits.Plant communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models in GS lies in their low robustness and poor interpretability. To address these challenges, we developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks. This framework combines convolutional neural networks with multiple self-attention mechanisms to improve accuracy. The ability of Cropformer to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops: maize, rice, wheat, foxtail millet, and tomato. Evaluation results show that Cropformer outperforms other GS methods in both precision and robustness, achieving up to a 7.5% improvement in prediction accuracy compared to the runner-up model. Additionally, Cropformer enhances the analysis and mining of genes associated with traits. We identified numerous single nucleotide polymorphisms (SNPs) with potential effects on maize phenotypic traits and revealed key genetic variations underlying these differences. Cropformer represents a significant advancement in predictive performance and gene identification, providing a powerful general tool for improving genomic design in crop breeding. Cropformer is freely accessible at https://cgris.net/cropformer.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.