FannLab/PairNet: PairNet v1.0 – Initial Release
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866902156678791168 |
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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 |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| 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 |