Correlation-driven framework based on graph convolutional network for clinical disease classification

DOI10.1080/00949655.2021.1921777OpenAlexW3163401569MaRDI QIDQ3389659

Y. Xiao, Muzhou Hou, Kai Cao

Publication date: 23 March 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2021.1921777

zbMATH Keywords

random forestgraph generationdisease classificationgraph convolutional networkstructured features

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• UCI-ml
• TensorFlow

Cites Work

• Unnamed Item
• Bagging predictors
• Model selection and estimation in the matrix normal graphical model
• A gentle introduction to deep learning for graphs
• Gemini: graph estimation with matrix variate normal instances
• Graph Estimation for Matrix-variate Gaussian Data
• Information-Geometric Measures as Robust Estimators of Connection Strengths and External Inputs
• Random forests

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