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Convolutional neural network model to predict causal risk factors that share complex regulatory features
Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural net...
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| 出版年: | Nucleic Acids Res |
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| 主要な著者: | , , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Oxford University Press
2019
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6902027/ https://ncbi.nlm.nih.gov/pubmed/31598692 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/nar/gkz868 |
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