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Found 3 papers, 1 papers with code
forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction
To address this limitation and develop a robust classification model without relying on external knowledge, we propose a \underline{for}est \underline{g}raph-\underline{e}mbedded deep feedforward \underline{net}work (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task.
A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data
This "$n<<p$" property has hampered application of deep learning techniques for disease outcome classification.
A Bayesian Method for Joint Clustering of Vectorial Data and Network Data
We present a new model-based integrative method for clustering objects given both vectorial data, which describes the feature of each object, and network data, which indicates the similarity of connected objects.
