Cargando…
Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions
MOTIVATION: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that im...
Autores principales: | Yan, Zichao, Hamilton, William L, Blanchette, Mathieu |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355240/ https://www.ncbi.nlm.nih.gov/pubmed/32657407 http://dx.doi.org/10.1093/bioinformatics/btaa456 |
Ejemplares similares
-
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction
por: You, Ronghui, et al.
Publicado: (2021) -
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function
por: Boadu, Frimpong, et al.
Publicado: (2023) -
A gated graph transformer for protein complex structure quality assessment and its performance in CASP15
por: Chen, Xiao, et al.
Publicado: (2023) -
Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data
por: Huang, Daiyun, et al.
Publicado: (2021) -
RNAMotifComp: a comprehensive method to analyze and identify structurally similar RNA motif families
por: Rahaman, Md Mahfuzur, et al.
Publicado: (2023)