Cargando…
“Guilt by association” is not competitive with genetic association for identifying autism risk genes
Discovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have...
Autores principales: | Gunning, Margot, Pavlidis, Paul |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342445/ https://www.ncbi.nlm.nih.gov/pubmed/34354131 http://dx.doi.org/10.1038/s41598-021-95321-y |
Ejemplares similares
-
The Impact of Multifunctional Genes on "Guilt by Association" Analysis
por: Gillis, Jesse, et al.
Publicado: (2011) -
“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
por: Gillis, Jesse, et al.
Publicado: (2012) -
Guilt by association
por: Petsko, Gregory A
Publicado: (2009) -
Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function
por: Tian, Weidong, et al.
Publicado: (2008) -
Metagenomic Guilt by Association: An Operonic Perspective
por: Vey, Gregory
Publicado: (2013)