ArticleFrontiers in plant science2025
SaGP: identifying plant saline-alkali tolerance genes based on machine learning techniques.
Article in Frontiers in plant science, 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.
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
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Authors and funding
11 authors.
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Abstract
Mining novel genes underlying agronomical traits is a crucial subject in plant biology, essential for enhancing crop quality, ensuring food security, and preserving biodiversity. Wet experiments are the main methods to uncover genes with target functions but are expensive and time-consuming. Machine learning, in contrast, can accelerate the gene discovery process by learning from accumulated data, making it more efficient and cost-effective. However, despite their potential, existing machine-learning tools to mine stress-resistant genes in plants are scarce. In this study, we developed the first known machine learning model, SaGP (Saline-alkali Genes Prediction), to identify plant saline-alkali tolerance genes based on sequencing data. It outperformed traditional computational tools,
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