Chi Zhang

The paper identifies increased MCT4-associated lactate export in myeloid cells during HNSCC progression using single-cell RNA-sequencing and metabolic flux analysis.

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 paper introduces EmitGCL, a deep-learning framework that predicts future metastasis in cancer with high accuracy, identifies biomarkers, and validates therapeutic targets.

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

This paper develops TDMA, a transcriptomics-based model to estimate DNA methylation activity and epigenetic aging from bulk, single-cell, and spatial transcriptomics data.

The paper develops a machine learning model to predict SARS-CoV-2 mutation trends using historical data, achieving low prediction error and robustness across mutation patterns.

This study uses microfluidic platforms to model human retinal ganglion cell compartmentalization, showing axonal deficits in disease and astrocyte reactivity-induced neurodegeneration.

S3R models spatially varying associations in spatial transcriptomics data by estimating location-specific coefficients with structured sparsity and smoothness penalties, showing effectiveness in synthetic and real datasets.

This study shows that the CX3CR1-V249I variant in human microglia leads to dysfunctional phenotypes and affects neuronal growth and function.

This paper introduces DeepSAS, a deep graph framework to identify senescent cells and their associated genes, revealing cell-type-specific senescence features in aging and disease.

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.

This study uses spatial transcriptomics to analyze a spinal ependymoma tumor, revealing cellular heterogeneity and potential developmental hierarchy within the ependymal cell lineage.

This study uses TCGA transcriptomics data to analyze metabolic shifts in 11 cancer subtypes and their adjacent normal tissues, revealing patterns in glucose uptake, glycolysis, TCA cycle, and glutaminolysis.

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.

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.

The paper integrates spatial transcriptomics and single cell sequencing to identify immune and epithelial cell cross-talk patterns in acute kidney injury models.

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.

This study shows that hPSC-derived RGCs with the OPTN(E50K) mutation display neurodegenerative phenotypes, providing insights into glaucoma mechanisms.

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 DESSO, a deep learning framework for predicting regulatory motifs from ChIP-Seq data, showing improved accuracy over existing tools.

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.

The paper investigates the role of extracellular matrix (ECM) and immune components in Oral Squamous Cell Carcinoma (OSCC), finding that ECM upregulation and immune cell infiltration are key factors in tumor invasion.

The paper introduces a probabilistic bi-clustering method for single-cell transcriptomic data to detect sub-populations with shared or distinct expression patterns.