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...

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Detalles Bibliográficos
Autores principales: Lumbanraja, Favorisen Rosyking, Mahesworo, Bharuno, Cenggoro, Tjeng Wawan, Sudigyo, Digdo, Pardamean, Bens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Bioinformatics
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

Internet

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

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