Cargando…
Recurrent Neural Network for Predicting Transcription Factor Binding Sites
It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome these problems, some computational methods, based on...
Autores principales: | Shen, Zhen, Bao, Wenzheng, Huang, De-Shuang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6189047/ https://www.ncbi.nlm.nih.gov/pubmed/30323198 http://dx.doi.org/10.1038/s41598-018-33321-1 |
Ejemplares similares
-
Locating transcription factor binding sites by fully convolutional neural network
por: Zhang, Qinhu, et al.
Publicado: (2021) -
CIPPN: computational identification of protein pupylation sites by using neural network
por: Bao, Wenzheng, et al.
Publicado: (2017) -
Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture
por: Wang, Siguo, et al.
Publicado: (2021) -
Novel human microbe-disease association prediction using network consistency projection
por: Bao, Wenzheng, et al.
Publicado: (2017) -
DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks
por: Chen, Chen, et al.
Publicado: (2021)