Sha Cao

Latest

• Supervised clustering of high dimensional data using regularized mixture modeling
• A New Algorithm using Component-wise Adaptive Trimming For Robust Mixture Regression
• SSMD- A semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data
• RobMixReg-an R package for robust, flexible and high dimensional mixture regression
• Geometric All-Way Boolean Tensor Decomposition
• scFEA-A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data
• Denoising individual bias for a fairer binary submatrix detection
• Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation.
• M3S - A comprehensive model selection for multi-modal single-cell RNA sequencing data. BMC Bioinformatics
• LTMG-a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
• A semi supervised deconvolution method for quantifying the composition and activity of tumor infiltrating cell types.
• Inference of Cell Types and Deconvolution – a next-generation deconvolution method for accurate assess cell population and activities in tumor microenvironment.
• Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification.