ArticleFrontiers in genetics2020
Sparse Convolutional Neural Networks for Genome-Wide Prediction.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 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
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Genomic prediction of feed efficiency in boars by deep learning.G3 (Bethesda, Md.) · 2026Article
- Using genomic context informed genotype data and within-model ancestry adjustment to classify type 2 diabetes.Communications medicine · 2025Article
- Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow.Plant methods · 2025Article
- Can oxidative potential be a plant risk indicator for heavy metals contaminated soil? Analysis of ryegrass (Eco-Environment & Health · 2025Article
- Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs.Frontiers in genetics · 2025Article
- Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.G3 (Bethesda, Md.) · 2024Article
- Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.BMC bioinformatics · 2024Article
- Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.Animal bioscience · 2024Article
- TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.BMC genomics · 2024Article
- Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models.Frontiers in genetics · 2024Article
- A review of machine learning models applied to genomic prediction in animal breeding.Frontiers in genetics · 2023Review
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- A divide-and-conquer approach for genomic prediction in rubber tree using machine learning.Scientific reports · 2022Article
- Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information.Journal of agricultural and food chemistry · 2022Article
- A joint learning approach for genomic prediction in polyploid grasses.Scientific reports · 2022Article
- Can We Convert Genotype Sequences Into Images for Cases/Controls Classification?Frontiers in bioinformatics · 2022Article
- Genomic Prediction Methods Accounting for Nonadditive Genetic Effects.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide prediction (GWP) has become the state-of-the art method in artificial selection. Data sets often comprise number of genomic markers and individuals in ranges from a few thousands to millions. Hence, computational efficiency is important and various machine learning methods have successfully been used in GWP. Neural networks (NN) and deep learning (DL) are very flexible methods that usually show outstanding prediction properties on complex structured data, but their use in GWP is nevertheless rare and debated. This study describes a powerful NN method for genomic marker data that can easily be extended. It is shown that a one-dimensional convolutional neural network (CNN) can be used to incorporate the ordinal information between markers and, together with pooling and
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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.