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Unsupervised Ensemble Learning for Efficient Integration of Pre-trained Polygenic Risk Scores

2025-01-06 genetic and genomic medicine Title + abstract only
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The growing availability of pre-trained polygenic risk score (PRS) models has enabled their integration into real-world applications, reducing the need for extensive data labeling, training, and calibration. However, selecting the most suitable PRS model for a specific target population remains challenging, due to issues such as limited transferability, data heterogeneity, and the scarcity of observed phenotype in real-world settings. Ensemble learning offers a promising avenue to enhance the pr...

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