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NCNet: Deep Learning Network Models for Predicting Function of Non-coding DNA
The human genome consists of 98.5% non-coding DNA sequences, and most of them have no known function. However, a majority of disease-associated variants lie in these regions. Therefore, it is critical to predict the function of non-coding DNA. Hence, we propose the NCNet, which integrates deep resid...
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| Опубликовано в: : | Front Genet |
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| Главные авторы: | , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Frontiers Media S.A.
2019
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6549219/ https://ncbi.nlm.nih.gov/pubmed/31191597 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fgene.2019.00432 |
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