Finding gene network topologies for given biological function with recurrent neural network
Networks are useful ways to describe interactions between molecules in a cell, but predicting the real topology of large networks can be challenging. Here, the authors use deep learning to predict the topology of networks that perform biologically-plausible functions.
Guardado en:
Autores principales: | Jingxiang Shen, Feng Liu, Yuhai Tu, Chao Tang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/8d4a9caf655a48a7a4ae61cbbeee9d44 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Direction Finding Using GHA Neural Networks
por: N. H. Abbas
Publicado: (2017) -
Direction Finding Using GHA Neural Networks
por: N. H. Abbas
Publicado: (2006) -
Hidden neural networks for transmembrane protein topology prediction
por: Ioannis A. Tamposis, et al.
Publicado: (2021) -
A networked smart home system based on recurrent neural networks and reinforcement learning
por: Zhongwang Li, et al.
Publicado: (2021) -
CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
por: Ali Masoudi-Nejad, et al.
Publicado: (2012)