ArticleGenetics, selection, evolution : GSE2018
Approximate Bayesian neural networks in genomic prediction.
Article in Genetics, selection, evolution : GSE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 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
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 2025Article
- Incorporating information of causal variants in genomic prediction using GBLUP or machine learning models in a simulated livestock population.Journal of animal science and biotechnology · 2025Article
- A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics.Metabolites · 2025Article
- Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs.Frontiers in genetics · 2025Article
- Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.Animal bioscience · 2024Article
- Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches.Frontiers in plant science · 2024Article
- Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection.International journal of molecular sciences · 2023Article
- A review of machine learning models applied to genomic prediction in animal breeding.Frontiers in genetics · 2023Review
- Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data.Genetics · 2022Article
- Article
- Predicting ecological footprint based on global macro indicators in G-20 countries using machine learning approaches.Environmental science and pollution research international · 2022Article
- Article
- NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction.Frontiers in plant science · 2022Article
- The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock.Animals : an open access journal from MDPI · 2021Review
- Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies.G3 (Bethesda, Md.) · 2021Article
- DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies.NAR genomics and bioinformatics · 2021Article
- Modeling and Predicting the Cell Migration Properties from Scratch Wound Healing Assay on Cisplatin-Resistant Ovarian Cancer Cell Lines Using Artificial Neural Network.Healthcare (Basel, Switzerland) · 2021Article
- Machine learning in plant science and plant breeding.iScience · 2021Review
Corrections and comments
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1 author.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-wide marker data are used both in phenotypic genome-wide association studies (GWAS) and genome-wide prediction (GWP). Typically, such studies include high-dimensional data with thousands to millions of single nucleotide polymorphisms (SNPs) recorded in hundreds to a few thousands individuals. Different machine-learning approaches have been used in GWAS and GWP effectively, but the use of neural networks (NN) and deep-learning is still scarce. This study presents a NN model for genomic SNP data.
resultsWe show, using both simulated and real pig data, that regularization is obtained using weight decay and dropout, and results in an approximate Bayesian (ABNN) model that can be used to obtain model averaged posterior predictions. The ABNN model is implemented in mxnet and shown to yield better prediction accuracy than genomic best linear unbiased prediction and Bayesian LASSO. The mean squared error was reduced by at least 6.5% in the simulated data and by at least 1% in the real data. Moreover, by comparing NN of different complexities, our results confirm that a shallow model with one layer, one neuron, one-hot encoding and a linear activation function performs better than more complex models.
conclusionsThe ABNN model provides a computationally efficient approach with good prediction performance and in which the weight components can also provide information on the importance of the SNPs. Hence, ABNN is suitable for both GWP and GWAS.
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