Fuchu He

The paper introduces Plasmix, a plasma reference suite, to improve cross-platform proteomics integration by identifying proteins with consistent titration responses across platforms.

The paper introduces SWIFT, a fast and efficient proteomic workflow for processing microgram-level FF and FFPE tissues, achieving high repeatability and deep proteome coverage.

quantms-rescoring improves proteomics analysis by integrating multiple search engines and machine learning, increasing identified spectra and quantifying more phosphorylated peptides and modification sites.

The paper introduces an automated system for high-throughput proteomic data generation, demonstrating its robustness and application in spatial proteomics and plasma/cell line profiling.

The paper introduces Cell Decoder, a deep learning model that provides multi-scale interpretability for cell type decoding and data integration, showing its effectiveness in human and mouse datasets.

DeepRTAlign improves retention time alignment for large cohort LC-MS data, enhancing identification sensitivity without compromising quantitative accuracy and enabling predictive modeling for hepatocellular carcinoma recurrence.