Cargando…
Analysis of Ensemble Feature Selection for Correlated High-Dimensional RNA-Seq Cancer Data
Discovery of diagnostic and prognostic molecular markers is important and actively pursued the research field in cancer research. For complex diseases, this process is often performed using Machine Learning. The current study compares two approaches for the discovery of relevant variables: by applic...
Autores principales: | Polewko-Klim, Aneta, Rudnicki, Witold R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304026/ http://dx.doi.org/10.1007/978-3-030-50420-5_39 |
Ejemplares similares
-
Robust Data Integration Method for Classification of Biomedical Data
por: Polewko-Klim, Aneta, et al.
Publicado: (2021) -
Bootstrap Bias Corrected Cross Validation Applied to Super Learning
por: Mnich, Krzysztof, et al.
Publicado: (2020) -
Integration of multiple types of genetic markers for neuroblastoma may contribute to improved prediction of the overall survival
por: Polewko-Klim, Aneta, et al.
Publicado: (2018) -
Sensitivity analysis based on the random forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken
por: Polewko-Klim, Aneta, et al.
Publicado: (2020) -
Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data
por: Sun, Xiaoxiao, et al.
Publicado: (2020)