SteroidXtract: Deep Learning-Based Pattern Recognition Enables Comprehensive and Rapid Extraction of Steroid-Like Metabolic Features for Automated Biology-Driven Metabolomics
Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome
System Biology-Guided Chemical Proteomics to Discover Protein Targets of Monoethylhexyl Phthalate in Regulating Cell Cycle
EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained with over 25000 Extracted Ion Chromatograms
DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data