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Method: Automated interpretable artificial intelligence genomic prediction with AIGP
- Lei Wei,
- Ziqin Jiang,
- Baoliang Fan,
- Yidan Yan,
- Zhenqiang Xu,
- Xiaoxiang Hu,
- and Yuzhe Wang
Genome Res. March 5, 2026 gr.281006.125; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
..., Nguyen HT, Batley J, Bayer 591 PE, Edwards D. 2022. Machine learning models outperform deep learning models, 592 provide interpretation and facilitate feature selection for soybean trait prediction. BMC 593 Plant Biol 22: 180. 594 He K, Yu T, Gao S, Chen S, Li L, Zhang X, Huang C, Xu Y, Wang J, Prasanna...
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Method: ML-MAGES enables multivariate genetic association analyses with genes and effect size shrinkage
- Xiran Liu,
- Lorin Crawford,
- and Sohini Ramachandran
Genome Res. December 2025 35: 2691-2700; Published in Advance November 19, 2025, doi:10.1101/gr.280440.125
...-MAGESInspired by the single-trait gene-ε framework, we propose ML-MAGES to advance multivariate GWA analyses. Our method introduces two key innovations: (1) using deep learning for efficient effect size shrinkage to control for inflation and (2) using variational inference for flexible clustering of multitrait associations...
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March 2026, 36 (3)