Cargando…
Identifying Intrinsically Disordered Protein Regions through a Deep Neural Network with Three Novel Sequence Features
The fast, reliable, and accurate identification of IDPRs is essential, as in recent years it has come to be recognized more and more that IDPRs have a wide impact on many important physiological processes, such as molecular recognition and molecular assembly, the regulation of transcription and tran...
Autores principales: | Zhao, Jiaxiang, Wang, Zengke |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950681/ https://www.ncbi.nlm.nih.gov/pubmed/35330096 http://dx.doi.org/10.3390/life12030345 |
Ejemplares similares
-
Deep neural networks identify sequence context features predictive of transcription factor binding
por: Zheng, An, et al.
Publicado: (2021) -
Identifying molecular features that are associated with biological function of intrinsically disordered protein regions
por: Zarin, Taraneh, et al.
Publicado: (2021) -
Evaluation of Sequence Features from Intrinsically Disordered Regions for the Estimation of Protein Function
por: Sharma, Alok, et al.
Publicado: (2014) -
Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks
por: He, Hao, et al.
Publicado: (2021) -
Retracted: Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks
por: Intelligence and Neuroscience, Computational
Publicado: (2023)