Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.
This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) we...
Guardado en:
Autores principales: | Yongxia Zhou, Fang Yu, Timothy Duong |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2014
|
Materias: | |
Acceso en línea: | https://doaj.org/article/ddbb3aaf02184626bce3a2c82755653f |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI
por: Asim Mazin, et al.
Publicado: (2021) -
Astroglia in Autism Spectrum Disorder
por: Kinga Gzielo, et al.
Publicado: (2021) -
The pathogenesis of autism spectrum disorder
por: Beatriz Sanabria-Barradas, et al.
Publicado: (2021) -
Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting
por: John Virostko, et al.
Publicado: (2021) -
Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI
por: Bin Wang, et al.
Publicado: (2021)