Xiaojun Wu

The paper develops mathematical models and Bayesian inference methods to analyze tumor-immune dynamics in metastatic breast cancer, revealing immune suppressive parameters as key predictors of response to immunotherapy.

The paper investigates how myeloid-T cell interactions and B cell activation influence response to checkpoint inhibition in metastatic breast cancer using multi-omics and computational methods.

This paper introduces a framework for model discovery and selection in noisy biological systems using hybrid dynamical systems and neural networks, showing improved performance over existing methods.

The paper develops Bayesian methods for inferring dynamic parameters in single cells using gene expression and Ca2+ data, showing that transfer learning improves inference when cells are ordered by transcriptional similarity.

This study identifies synaptotagmin-7 as a Ca2+ sensor in splanchnic nerve terminals that regulates acetylcholine release and short-term presynaptic facilitation.

The paper analyzes transcriptomic data from pilocarpine-induced temporal lobe epilepsy to identify predictive biomarkers and cellular mechanisms, revealing aberrant interactions between cell types and potential drug targets.

This study examines the relationship between astrocyte subtypes, neurological and psychiatric diseases, and psychotropic medications using gene enrichment analysis and qPCR, finding that astrocytes are not a primary target of these medications.

This study compares psychiatric disorders using pathway, cell-type, drug-target, and chromosomal analyses to identify common and distinct features across an unbiased cluster of disorders.

The paper identifies lamina-specific gene expression changes in pyramidal neurons from schizophrenia patients compared to controls, highlighting immune and synaptic pathway disruptions.

This paper develops a computational strategy to identify novel targets and therapeutic compounds for opioid dependence using time course gene expression data from mouse striatum.