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Bioinformatics (Oxford, England)|June 1, 2018
A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression dataYunchuan Kong, Tianwei YuGenomics|October 6, 2011
Capturing changes in gene expression dynamics by gene set differential coordination analysisTianwei Yu, Yun BaiBriefings in Bioinformatics|April 21, 2025
Nonlinear embedding and integration of omics data: a fast and tuning-free approachShengjie Liu, Tianwei YuCurrent Metabolomics|September 7, 2013
Analyzing LC/MS metabolic profiling data in the context of existing metabolic networksTianwei Yu, Yun BaiBioinformatics (Oxford, England)|February 14, 2020
scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustmentTeng Fei, Tianwei YuBMC Genomics|May 24, 2019
A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scaleYunchuan Kong, Tianwei YuBioinformatics (Oxford, England)|March 13, 2020
forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph constructionYunchuan Kong, Tianwei YuBMC Bioinformatics|December 2, 2014
Network-based modular latent structure analysisTianwei Yu, Yun BaiBMC Genomics|November 18, 2011
Improving gene expression data interpretation by finding latent factors that co-regulate gene modules with clinical factorsTianwei Yu, Yun BaiScientific Reports|November 9, 2018
A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data ClassificationYunchuan Kong, Tianwei YuPageof 13