SSMFN: a fused spatial and sequential deep learning model for methylation site prediction
Background Conventional in vivo methods for post-translational modification site prediction such as spectrophotometry, Western blotting, and chromatin immune precipitation can be very expensive and time-consuming. Neural networks (NN) are one of the computational approaches that can predict effectiv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2021-08-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-683.pdf |