Wei Ping Jia

MetAgeFormer is a transformer-based model pre-trained on metabolomic data to estimate biological age and identify metabolic subtypes associated with disease risk.

This study identifies BCKDK as a hypoxia-responsive kinase that worsens cerebral ischemia injury by disrupting BCAA catabolism and exacerbating neuronal damage.

The paper identifies human myelocyte and metamyelocyte-stage neutrophils as immunosuppressive cells that promote cancer progression and suggests targeting them for immunotherapy.

This study identifies associations between bile acid profiles and symptoms of depression and anxiety in MDD patients, finding higher anxiety correlates and treatment failure predictors.

This study shows that caloric restriction increases malate and related metabolites, remodels gut microbiota energy metabolism, and promotes host autophagy through a functional metabolic network.

The paper identifies metabolites in umbilical cord blood linked to maternal pre-pregnant obesity using metabolomics in a multi-ethnic cohort.

This study identifies metabolites and pathways in serum and brain tissue associated with cognitive impairment and develops models to predict antecedent cognitive decline in older adults.

The paper identifies differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease by analyzing transcriptomes and metabolomics data from brain samples.

This study shows that hyocholic acid species are associated with metabolic health and glucose homeostasis, and their levels predict diabetes risk in humans.

The paper identifies altered bile acid profiles in Alzheimer's disease, linking them to cognitive impairment and genetic variants through a large study.

The paper identifies associations between serum bile acid metabolites and AD biomarkers in the A/T/N framework, suggesting a potential role for bile acid pathways in AD pathophysiology.

This study shows that Chinese Baijiu reduces liver injury in mice by altering gut microbiota and metabolome compared to pure ethanol.

GSimp is a Gibbs sampler-based method for imputing left-censored missing values in metabolomics data, showing superior performance in accuracy and statistical analysis.