Cargando…
Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks
Intrinsically disordered proteins (IDPs) possess at least one region that lacks a single stable structure in vivo, which makes them play an important role in a variety of biological functions. We propose a prediction method for IDPs based on convolutional neural networks (CNNs) and feature selection...
Autores principales: | He, Hao, Yang, Yong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727116/ https://www.ncbi.nlm.nih.gov/pubmed/34992646 http://dx.doi.org/10.1155/2021/4455604 |
Ejemplares similares
-
Retracted: Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks
por: Intelligence and Neuroscience, Computational
Publicado: (2023) -
Prediction of MoRFs based on sequence properties and convolutional neural networks
por: He, Hao, et al.
Publicado: (2021) -
Functional annotation of creeping bentgrass protein sequences based on convolutional neural network
por: Jiang, Han-Yu, et al.
Publicado: (2022) -
Protein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks
por: Xu, Weixia, et al.
Publicado: (2021) -
Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest
por: Wang, Lei, et al.
Publicado: (2019)