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Policy gradient-guided ensemble learning for enhanced polygenic risk prediction in ultra-high-dimensional genomics

2025-09-25 genetic and genomic medicine Title + abstract only
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Polygenic diseases challenge genetic risk prediction due to extreme dimensionality, low per-variant effect sizes, and non-additive interactions. Conventional marginal P-value-based methods potentially overlook subtle signals and complex dependencies, while inefficient random sampling in ensembles misses sparse signals. We introduce ELAG, an ensemble learning framework that advances feature bagging by reformulating variant selection as an approximate reinforcement learning problem. Leveraging pol...

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