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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. April 2026 36: 814-826; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
...), and (3) incorporation of in silico mutational landscapes (Chen et al. 2022) and gene-editing data sets. Multimodal integration will advance phenotype prediction from single-source models to large-scale pretrained models. With self-supervised learning and causal inference, large language models play...
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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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