Cheng Zhang

AlphaGEM constructs genome-scale metabolic models using structural alignment and deep learning, improving accuracy over existing methods for diverse species.

The paper constructs tissue- and cell-type-specific metabolic models to study the effects of a high-sugar, high-fat diet on liver metabolism, revealing metabolic reprogramming and dysfunction.

The paper decodes correlated dynamics at the pMHC interface to understand TCR recognition and immunogenicity using computational methods.

This study uses scRNA-seq to characterize periodontal granulation tissue, identifying novel cell populations and pathways involved in disease pathogenesis and regenerative potential.

This study investigates the effects of JNK inhibition on metabolic dysfunction caused by sucrose overconsumption in rats using multi-tissue network analysis.

This paper develops a framework integrating scRNA-seq with mIHC to create a high-resolution proteomic atlas of human spermatogenesis, revealing discrepancies between mRNA and protein expression.

This study identifies immunomodulatory peptides deregulated in breast cancer using single-cell and spatial transcriptomics, and proposes drugs to modulate their expression.

This study identifies PKLR as a therapeutic target for NAFLD and validates two drug candidates through in vitro and in vivo experiments.

The paper annotates all human protein-coding genes with their function and spatial distribution across cell types using single-cell transcriptomics data and introduces a new classification tool.

This study identifies dysregulated transcriptional regulators in NAFLD using network analysis and integrates transcriptomic data with metabolic models.

This study identifies retinoids and sex hormone dysregulation as ageing-related features in Alzheimer's and Parkinson's disease subclasses using transcriptomic and metabolic analyses.

The paper analyzes transcriptomic changes in heart and other metabolically active tissues after myocardial infarction in mice, identifying common and tissue-specific biological responses.

iNetModels 2.0 provides an interactive platform for visualizing and exploring multi-omics associations in human health and disease.

The paper investigates the prognostic significance of PKM transcripts in 25 cancers and identifies a prognostic signature in KIRC, revealing functional roles of alternatively spliced PKM transcripts.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

The paper identifies two ccRCC subtypes based on PKM alternative splicing and proposes drug repurposing for each subtype.

RMetD2 integrates differentially expressed genes into genome-scale metabolic models to reveal metabolic differences between conditions, outperforming existing methods in capturing experimentally observed features.