ArticleFrontiers in genetics2019
Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.
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Who cites it
89 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- BiMSGP: A Bidirectional Mamba-Based Model for Scalable and Accurate Genomic Prediction in Plants.Genes · 2026Article
- A Boruta-SMOTE Integrated Approach for Rapid Donkey Breed Classification Using SNP Data: Addressing High-Dimensionality and Small Sample Challenges.Biochemical genetics · 2026Article
- Robustly enhancing crop genomic prediction accuracy through ensemble learning and iterative optimization.Nature communications · 2026Article
- Achieving High-Density and Stress-Resilient Maize Breeding Via Germplasm Innovation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Whole genome sequencing-based multi-locus association mapping for kernel iron, zinc and protein content in groundnut.Scientific reports · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.International journal of molecular sciences · 2026Review
- CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Briefings in bioinformatics · 2026Article
- Interpreting artificial neural networks to detect genome-wide association signals for complex traits.NAR genomics and bioinformatics · 2026Article
- MoEGP: an efficient crop genomic prediction approach based on the mixture of experts network.Plant methods · 2026Article
- Genomic prediction of feed efficiency in boars by deep learning.G3 (Bethesda, Md.) · 2026Article
- G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data.Biomolecules · 2025Article
- deepBreaks identifies and prioritizes genotype-phenotype associations using machine learning.Scientific reports · 2025Article
- Accelerated breeding modernization: a global blueprint for driving genetic gains, climate resilience, and food security in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Article
- Decoding ancestry-specific genetic risk: interpretable deep feature selection reveals prostate cancer SNP disparities in diverse populations.BioData mining · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.
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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.