Wennan Chang

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

The paper uses computational modeling and transcriptomic data to show that cancer cell phenotypes can be explained by Fenton reaction-driven proton production and reprogrammed metabolisms.

scFLUX predicts single-cell metabolic fluxome and variations using transcriptomics data, offering a web server and local tools for individual cell or sample-level analysis.

This paper introduces scFEA, a graph neural network model to estimate cell-wise metabolic flux from scRNA-seq data, validated with matched metabolomics data and applied to multiple datasets.

SSMD improves cell type identification and deconvolution in mouse transcriptomics by addressing variations in genetics, experimental platforms, and small sample sizes.

RobMixReg provides an R package for robust, flexible, and high-dimensional mixture regression to address outliers and complex regression forms.

ICTD improves tissue microenvironment analysis by enhancing cell type identification, marker gene discovery, and functional inference through a semi-supervised deconvolution approach.

The paper introduces LTMG, a model for capturing transcriptional regulatory heterogeneities in scRNA-seq data, showing improved fitting and validation on multiple datasets.

This paper introduces a bi-clustering framework to characterize colorectal cancer subtypes and identify prognostic markers and drug resistance mechanisms beyond CMS classification.