ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Protein engineering is an active area of research in which machine learning has proven quite powerful. Here, the authors present a deep learning method that integrates both general and protein-specific sequence representations to improve the engineering of one’s protein of interest.
Guardado en:
Autores principales: | Yunan Luo, Guangde Jiang, Tianhao Yu, Yang Liu, Lam Vo, Hantian Ding, Yufeng Su, Wesley Wei Qian, Huimin Zhao, Jian Peng |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/0bb0a79962b04f8bba4f09b68761698c |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Sequence-to-function deep learning frameworks for engineered riboregulators
por: Jacqueline A. Valeri, et al.
Publicado: (2020) -
Exploration of machine algorithms based on deep learning model and feature extraction
por: Yufeng Qian
Publicado: (2021) -
Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
por: Luke Oakden-Rayner, et al.
Publicado: (2017) -
Getting the Hologenome Concept Right: an Eco-Evolutionary Framework for Hosts and Their Microbiomes
por: Kevin R. Theis, et al.
Publicado: (2016) -
BreakNet: detecting deletions using long reads and a deep learning approach
por: Junwei Luo, et al.
Publicado: (2021)