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2025
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| Online Access: | https://doi.org/10.5281/zenodo.17338730 |
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| _version_ | 1866902221524828160 |
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| author | ziangmeng |
| author_facet | ziangmeng |
| contents | <p>InsightGWAS is a Transformer-based deep learning framework designed to prioritize genetic variants associated with complex diseases using genome-wide association studies (GWAS) summary statistics and multi-modal genomic annotations.</p> <p>MDD-MA Transformer Model: Pre-trained on Major Depressive Disorder (MDD) GWAS data and fine-tuned on Migraine (MA) data.</p> <p>This project consists of four example: Python scripts for training, transfer learning,inference using a Transformer-based model and a baseline(without transfer) learning and inference .</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17338730 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ziangmeng/MA-MDD-Transformer-based-model-: Frist release of MA-MDD-Transformer-based-model- ziangmeng <p>InsightGWAS is a Transformer-based deep learning framework designed to prioritize genetic variants associated with complex diseases using genome-wide association studies (GWAS) summary statistics and multi-modal genomic annotations.</p> <p>MDD-MA Transformer Model: Pre-trained on Major Depressive Disorder (MDD) GWAS data and fine-tuned on Migraine (MA) data.</p> <p>This project consists of four example: Python scripts for training, transfer learning,inference using a Transformer-based model and a baseline(without transfer) learning and inference .</p> |
| title | ziangmeng/MA-MDD-Transformer-based-model-: Frist release of MA-MDD-Transformer-based-model- |
| url | https://doi.org/10.5281/zenodo.17338730 |