Permutation-based identification of important biomarkers for complex diseases via machine learning models
Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Here, the authors propose a computationally efficient Permutation-based Feature Importance Test to assist interpretation and selection of indivi...
Guardado en:
Autores principales: | Xinlei Mi, Baiming Zou, Fei Zou, Jianhua Hu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/a14b8c9fa4db459da160a337cbf281f4 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A Class of New Permutation Polynomials over F2n
por: Qian Liu, et al.
Publicado: (2021) -
Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers
por: Roshan Shafiha, et al.
Publicado: (2021) -
Permutable SOS (symmetry operational similarity)
por: Sang-Wook Cheong, et al.
Publicado: (2021) -
PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.
por: George Crowley, et al.
Publicado: (2021) -
Circular permutation in proteins.
por: Spencer Bliven, et al.
Publicado: (2012)