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seqgra: principled selection of neural network architectures for genomics prediction tasks
MOTIVATION: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their contributions to a mechanistic understanding of the b...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048673/ https://www.ncbi.nlm.nih.gov/pubmed/35191481 http://dx.doi.org/10.1093/bioinformatics/btac101 |