A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs
Predicting the functional consequences of non-coding genetic variants is a challenge. Here, He et al. present GenoNet, a semi-supervised method that combines information from experimentally confirmed regulatory variants with cell type- and tissue specific annotation for function prediction.
Guardado en:
Autores principales: | Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
|
Materias: | |
Acceso en línea: | https://doaj.org/article/684d88cf45c54d75a24c417383281dee |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A genome-wide scan statistic framework for whole-genome sequence data analysis
por: Zihuai He, et al.
Publicado: (2019) -
Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.
por: Matthew R Whiteway, et al.
Publicado: (2021) -
Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning
por: Jiayu Shang, et al.
Publicado: (2021) -
A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
por: Jason Ernst, et al.
Publicado: (2008) -
Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
por: Jiarui Chen, et al.
Publicado: (2021)