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Bibliographische Detailangaben
1. Verfasser: FannLab
Format: Recurso digital
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Veröffentlicht: Zenodo 2025
Online-Zugang:https://doi.org/10.5281/zenodo.14838228
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  • <h2>PairNet v1.0 – First Official Release</h2> <p>We are excited to announce <strong>PairNet v1.0</strong>, a powerful ensemble learning framework for <strong>polygenic risk score (PRS) optimization</strong>. PairNet integrates multiple PRS models using hierarchical pairwise learning, improving predictive accuracy.</p> <h3>Key Features</h3> <p><strong>Ensemble PRS Learning</strong> – Combines multiple PRS models to enhance accuracy.<br> <strong>Efficient & Scalable</strong> – Optimized for high-dimensional GWAS datasets.<br> <strong>Flexible Input</strong> – Supports integration with <strong>PRS-CS, LDpred, and other PRS methods</strong>.</p>