FannLab/PairNet: PairNet v1.0 – Initial Release

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Autore principale: FannLab
Natura: Recurso digital
Pubblicazione: Zenodo 2025
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author FannLab
author_facet FannLab
contents <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>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_14838228
institution Zenodo
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publishDate 2025
publisher Zenodo
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spellingShingle FannLab/PairNet: PairNet v1.0 – Initial Release
FannLab
<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>
title FannLab/PairNet: PairNet v1.0 – Initial Release
url https://doi.org/10.5281/zenodo.14838228