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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 effecti...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
PeerJ Inc.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409337/ https://www.ncbi.nlm.nih.gov/pubmed/34541311 http://dx.doi.org/10.7717/peerj-cs.683 |