Cargando…
Feature selection for high-dimensional temporal data
BACKGROUND: Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we exten...
Autores principales: | Tsagris, Michail, Lagani, Vincenzo, Tsamardinos, Ioannis |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778658/ https://www.ncbi.nlm.nih.gov/pubmed/29357817 http://dx.doi.org/10.1186/s12859-018-2023-7 |
Ejemplares similares
-
Constraint-based causal discovery with mixed data
por: Tsagris, Michail, et al.
Publicado: (2018) -
Feature selection with the R package
MXM
por: Tsagris, Michail, et al.
Publicado: (2019) -
Automated machine learning for genome wide association studies
por: Lakiotaki, Kleanthi, et al.
Publicado: (2023) -
Biomarker signature identification in “omics” data with multi-class outcome
por: Lagani, Vincenzo, et al.
Publicado: (2013) -
omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data
por: Karathanasis, Nestoras, et al.
Publicado: (2016)