ArticleBriefings in bioinformatics2023
A transformer-based genomic prediction method fused with knowledge-guided module.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.The New phytologist · 2026Article
- Advances in genomics-driven genetic decoding and genomic design breeding in tomato.Horticulture research · 2026Article
- Gsformer: a dual-architecture deep learning framework with CNN-self-attention and sparse-attention for genomic selection.Genetics, selection, evolution : GSE · 2026Article
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- Enhancing genomic prediction accuracy in Huaxi cattle through integration of transcriptomic data and a self-attention-based SNP selection strategy.BMC 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
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- MoEGP: an efficient crop genomic prediction approach based on the mixture of experts network.Plant methods · 2026Article
- Crop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.Plant methods · 2026Article
- Large language models in bioinformatics: a comprehensive survey.Frontiers in genetics · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- GViT-GP: injecting the genomic relationship matrix as an inductive bias into a vision transformer via cross-attention for genomic prediction.Frontiers in genetics · 2026Article
- ETNet: an interpretable transformer framework for enhancer-enhancer interaction prediction with cross-context transferability.Briefings in bioinformatics · 2025Article
- Article
- Next-generation translational genomics for developing future crops.Functional & integrative genomics · 2025Review
- AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- MDNN: memetic deep neural network for genomic prediction.Briefings in bioinformatics · 2025Article
- Fast-forwarding plant breeding with deep learning-based genomic prediction.Journal of integrative plant biology · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
Genomic prediction (GP) uses single nucleotide polymorphisms (SNPs) to establish associations between markers and phenotypes. Selection of early individuals by genomic estimated breeding value shortens the generation interval and speeds up the breeding process. Recently, methods based on deep learning (DL) have gained great attention in the field of GP. In this study, we explore the application of Transformer-based structures to GP and develop a novel deep-learning model named GPformer. GPformer obtains a global view by gleaning beneficial information from all relevant SNPs regardless of the physical distance between SNPs. Comprehensive experimental results on five different crop datasets show that GPformer outperforms ridge regression-based linear unbiased prediction (RR-BLUP), support vector regression (SVR), light gradient boosting machine (LightGBM) and deep neural network genomic prediction (DNNGP) in terms of mean absolute error, Pearson's correlation coefficient and the proposed metric consistent index. Furthermore, we introduce a knowledge-guided module (KGM) to extract genome-wide association studies-based information, which is fused into GPformer as prior knowledge. KGM is very flexible and can be plugged into any DL network. Ablation studies of KGM on three datasets illustrate the efficiency of KGM adequately. Moreover, GPformer is robust and stable to hyperparameters and can generalize to each phenotype of every dataset, which is suitable for practical application scenarios.
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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.