Michael Turewicz

DLDN-Bench benchmarks deep learning de novo peptide sequencing tools using human mass spectrometry data, showing performance improvements over traditional methods.

This study analyzes bipartite peptide-protein graphs in proteomics, showing how graph structures vary with data type and organism, and highlights the challenge of quantifying proteins without unique peptides.

The paper introduces GoMi, a manually curated gold standard corpus for miRNA Named Entity Recognition, to train and test machine learning and rule-based approaches.

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