Chengxin Dai

The paper introduces onsite, a framework for estimating false localization rates in phosphoproteomics, showing pyLucXor achieves high accuracy and localization count.

The paper introduces π-MSNet, a large-scale proteomics dataset for AI training and benchmarking, showing performance improvements when models are retrained on it.

This paper introduces a method to control false discovery rates in deep-learning-based de novo peptide sequencing, improving reliability of peptide identifications.

pmultiqc generates metadata-aware QC reports for proteomics data using standardized metrics and integrates with existing workflows and cloud services.

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

ibaqpy computes iBAQ values for proteomics data using SDRF metadata, improving scalability and reproducibility in large-scale experiments.

The paper reanalyzes human tissue proteomics data using pangenome references to identify novel peptides and variants, demonstrating the effectiveness of proteogenomics approaches with pangenome data.

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.