Junjie Tong

This paper introduces Aird-MSI, a new format for mass spectrometry imaging data that reduces storage size and improves decompression speed while maintaining high data precision.

The paper identifies radiosensitizers by docking natural products against AI-predicted and experimental DNA repair protein structures, finding compounds that effectively target tumor radioresistance.

The paper evaluates precision loss in metabolomics mass spectrometry data compression and provides thresholds to minimize impact on downstream analysis.

This paper introduces entropy-based decoy generation methods to improve FDR estimation in metabolomics and presents a tool for accurate FDR-controlled analysis.

The paper introduces Aird-ComboComp, a compression framework for mass spectrometry data that improves compression ratios and decompression speed compared to existing methods.