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Found 4 papers, 1 papers with code
Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments
Here we report a deep learning setup, named Autoencoder-based Integrative Multi-omics data Embedding (AIME), to extract data representation for omics data integrative analysis.
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.
Nonlinear variable selection with continuous outcome: a nonparametric incremental forward stagewise approach
We present a method of variable selection for the sparse generalized additive model.
