Changlin Wan

This paper introduces mmFEA, a kinetic-aware framework for estimating metabolic flux changes from transcriptomics and metabolomics data, showing improved accuracy over baseline methods.

This study develops a human neuronal chimera model to study Alzheimer's tau pathology, showing human-specific vulnerability and recapitulating mature AD features in vivo.

MetaKnogic-Alpha transforms biomedical literature into a hyper-relational knowledge base for metabolic reasoning, achieving high mechanistic accuracy through automated synthesis and biochemical grounding.

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.

IRIS-FGM is an R package for analyzing scRNA-Seq data to identify functional gene modules, cluster cells, and perform enrichment analysis, with support for Seurat objects.

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.

QUBIC2 improves biclustering for bulk and single-cell RNA-Seq by addressing sparsity and transcriptional regulation, outperforming existing tools in functional module detection and cell type classification.