ArticleBMC genomics2025
EasyGeSe - a resource for benchmarking genomic prediction methods.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Dissecting genetic architecture and improving machine learning‑based genomic prediction of flowering time in Osmanthus fragrans by integrating structural variants.The plant genome · 2026Article
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
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Authors and funding
4 authors.
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Abstract
backgroundGenomic prediction is a widely used method to predict phenotypes from genotypic data. Advances in both biological and computer science have enabled the generation of vast amounts of data and the development of new algorithms, specifically in the field of machine learning. However, systematic benchmarking of new genomic prediction methods, which is essential for objective evaluation and comparison, remains limited.
resultsHere, we present EasyGeSe, a tool that provides access to a curated collection of datasets for testing genomic prediction methods. This resource encompasses data from multiple species, including barley, common bean, lentil, loblolly pine, eastern oyster, maize, pig, rice, soybean and wheat, representing a broad biological diversity. We filtered and arranged these data in convenient formats, provided functions in R and Python for easy loading and benchmarked several modelling strategies for genomic prediction. Predictive performance, measured by Pearson's correlation coefficient (r), varied significantly by species and trait (p < 0.001), ranging from - 0.08 to 0.96, with a mean of 0.62. Comparisons among parametric, semi-parametric and non-parametric models revealed modest but statistically significant (p < 1e
conclusionsBy standardizing input data and evaluation procedures, this resource simplifies benchmarking and enables fair, reproducible comparisons of genomic prediction methods. It also broadens access to genomic prediction data, encouraging data scientists and interdisciplinary researchers to test novel modelling strategies.
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Registered trials
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