A Bayesian network approach incorporating imputation of missing data enables exploratory analysis of complex causal biological relationships.
Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method...
Guardado en:
Autores principales: | Richard Howey, Alexander D Clark, Najib Naamane, Louise N Reynard, Arthur G Pratt, Heather J Cordell |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/0f1187fea9e34709af36a64f09f121e9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
An evolutionary perspective on epistasis and the missing heritability.
por: Gibran Hemani, et al.
Publicado: (2013) -
Multiple imputation of maritime search and rescue data at multiple missing patterns.
por: Guobo Wang, et al.
Publicado: (2021) -
Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
por: Woonghee Lee, et al.
Publicado: (2021) -
Kernel weighted least square approach for imputing missing values of metabolomics data
por: Nishith Kumar, et al.
Publicado: (2021) -
The identification of trans-acting factors that regulate the expression of GDF5 via the osteoarthritis susceptibility SNP rs143383.
por: Catherine M Syddall, et al.
Publicado: (2013)