Cargando…
Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure,...
Autores principales: | Okinaga, Yuichi, Kyogoku, Daisuke, Kondo, Satoshi, Nagano, Atsushi J., Hirose, Kei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169869/ https://www.ncbi.nlm.nih.gov/pubmed/34075095 http://dx.doi.org/10.1038/s41598-021-90791-6 |
Ejemplares similares
-
Network Structure Predicts Changes in Perception Accuracy of Social Relationships
por: Daniel, João R., et al.
Publicado: (2018) -
Computational Study of Estrogen Receptor-Alpha Antagonist with Three-Dimensional Quantitative Structure-Activity Relationship, Support Vector Regression, and Linear Regression Methods
por: Chang, Ying-Hsin, et al.
Publicado: (2013) -
Drug sensitivity prediction with high-dimensional mixture regression
por: Li, Qianyun, et al.
Publicado: (2019) -
The joint lasso: high-dimensional regression for group structured data
por: Dondelinger, Frank, et al.
Publicado: (2018) -
Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity
por: Amamoto, Yoshifumi, et al.
Publicado: (2020)