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 understandi...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2022
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| Online Access: | https://hdl.handle.net/1721.1/143575 |