ArticlePlant biotechnology journal2025
Genomic prediction with NetGP based on gene network and multi-omics data in plants.
Article in Plant biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.The New phytologist · 2026Article
- Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Single-Cell and Spatial Omics Technologies in Rice Abiotic Stress Biology: A Methodological Review.International journal of molecular sciences · 2026Review
- Robustly enhancing crop genomic prediction accuracy through ensemble learning and iterative optimization.Nature communications · 2026Article
- Integrating multi-model GWAS prior information enhances genomic prediction of cold tolerance traits in Populus simonii.BMC plant biology · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Briefings in bioinformatics · 2026Article
- Enhancing genomic prediction accuracy in Huaxi cattle through integration of transcriptomic data and a self-attention-based SNP selection strategy.BMC bioinformatics · 2026Article
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- WheatGP, a genomic prediction method based on CNN and LSTM.Briefings in bioinformatics · 2025Article
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Authors and funding
15 authors.
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Abstract
Genomic selection (GS) is a new breeding strategy. Generally, traditional methods are used for predicting traits based on the whole genome. However, the prediction accuracy of these models remains limited because they cannot fully reflect the intricate nonlinear interactions between genotypes and traits. Here, a novel single nucleotide polymorphism (SNP) feature extraction technique based on the Pearson-Collinearity Selection (PCS) is firstly presented and improves prediction accuracy across several known models. Furthermore, gene network prediction model (NetGP) is a novel deep learning approach designed for phenotypic prediction. It utilizes transcriptomic dataset (Trans), genomic dataset (SNP) and multi-omics dataset (Trans + SNP). The NetGP model demonstrated better performance compared to other models in genomic predictions, transcriptomic predictions and multi-omics predictions. NetGP multi-omics model performed better than independent genomic or transcriptomic prediction models. Prediction performance evaluations using several other plants' data showed good generalizability for NetGP. Taken together, our study not only offers a novel and effective tool for plant genomic selection but also points to new avenues for future plant breeding research.
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