Cargando…
PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information
Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, severa...
Autores principales: | Liu, Xia, Wang, Minghui, Li, Ao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929166/ https://www.ncbi.nlm.nih.gov/pubmed/35310161 http://dx.doi.org/10.7717/peerj.12847 |
Ejemplares similares
-
DeepPhos: prediction of protein phosphorylation sites with deep learning
por: Luo, Fenglin, et al.
Publicado: (2019) -
PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information
por: Yang, Hangyuan, et al.
Publicado: (2021) -
A sequence-based, deep learning model accurately predicts RNA splicing branchpoints
por: Paggi, Joseph M., et al.
Publicado: (2018) -
DeepRaccess: high-speed RNA accessibility prediction using deep learning
por: Hara, Kaisei, et al.
Publicado: (2023) -
EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model
por: Gan, Mingxin, et al.
Publicado: (2019)