5 papers
Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments
Tianwei Yu
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This ta…
forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction
Yunchuan Kong, Tianwei Yu
A unique challenge in predictive model building for omics data has been the small number of samples versus the large amount of features . This "" property brings…
Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior
Qingpo Cai, Jian Kang, Tianwei Yu
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur he…
A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data
Yunchuan Kong, Tianwei Yu
Gene expression data represents a unique challenge in predictive model building, because of the small number of samples compared to the huge amount of features . This "$…
DCA: Dynamic Correlation Analysis
Tianwei Yu
In high-throughput data, dynamic correlation between genes, i.e. changing correlation patterns under different biological conditions, can reveal important regulatory mechanisms. Gi…