seqgra: principled selection of neural network architectures for genomics prediction tasks
Abstract 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP),
2022-06-28T16:33:56Z.
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| Subjects: | |
| Online Access: | Get fulltext |