ArticleMolecular breeding : new strategies in plant improvement2024
Multi-omics assists genomic prediction of maize yield with machine learning approaches.
Article in Molecular breeding : new strategies in plant improvement, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Machine learning for multi-omics data integration in crop improvement: a systematic review.BMC bioinformatics · 2026Pooled it
- Review
- Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.Functional & integrative genomics · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Multi-trait and multi-environment genomic prediction enhances yield components improvement in durum wheat.Frontiers in plant science · 2026Article
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields.Genes · 2025Review
- Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
- DeepCCR: large-scale genomics-based deep learning method for improving rice breeding.Plant biotechnology journal · 2024Article
- Multiomics Research: Principles and Challenges in Integrated Analysis.Biodesign research · 2024Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops. In this study, we utilized multi-omics datasets from 156 maize recombinant inbred lines, containing 2496 single nucleotide polymorphisms (SNPs), 46 image traits (i-traits) from 16 developmental stages obtained through an automatic phenotyping platform, and 133 primary metabolites. Based on benchmark tests with different types of prediction models, some machine learning methods, such as Partial Least Squares (PLS), Random Forest (RF), and Gaussian process with Radial basis function kernel (GaussprRadial), achieved better prediction for maize yield, albeit slight difference for method preferences among i-traits, genomic, and metabolic data. We found that better yield prediction may be caused by various capabilities in ranking and filtering data features, which is found to be linked with biological meaning such as photosynthesis-related or kernel development-related regulations. Finally, by integrating multiple omics data with the RF machine learning approach, we can further improve the prediction accuracy of grain yield from 0.32 to 0.43. Our research provides new ideas for the application of plant omics data and artificial intelligence approaches to facilitate crop genetic improvements. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01454-z.
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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.