Cargando…
DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network
BACKGROUND: Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key...
Autores principales: | Chen, Xiao, Cheng, Jianlin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019949/ https://www.ncbi.nlm.nih.gov/pubmed/35439931 http://dx.doi.org/10.1186/s12859-022-04683-1 |
Ejemplares similares
-
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
por: Guo, Zhiye, et al.
Publicado: (2022) -
DeepSF: deep convolutional neural network for mapping protein sequences to folds
por: Hou, Jie, et al.
Publicado: (2018) -
P_VggNet: A convolutional neural network (CNN) with pixel-based attention map
por: Liu, Kunhua, et al.
Publicado: (2018) -
Attention-Based Convolutional Neural Network for Ingredients Identification
por: Chen, Shi, et al.
Publicado: (2023) -
Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network
por: Sato, Rin, et al.
Publicado: (2019)