Mario Picciani studied bioinformatics (M.Sc.) at the Technical University of Munich and started his PhD in computational proteomics in 2021. His research focuses on the development of models for drug response prediction with proteomics and the discovery of drug modes of actions and target-dependent patterns in cell viability data. He is the main author of “Oktoberfest”, a fully open-source python package for rescoring and spectral library generation using deep learning models to improve quality and identifications in proteomics data and also coauthored followup publications extending functionality and support for various ion types, fragmentation methods, and mass spectrometers.
Publications
2024
Adams, Charlotte; Gabriel, Wassim; Laukens, Kris; Picciani, Mario; Wilhelm, Mathias; Bittremieux, Wout; Boonen, Kurt: Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF. Nature Communications 15 (1), 2024 more…
Gabriel, Wassim; Picciani, Mario; The, Matthew; Wilhelm, Mathias: Deep Learning-Assisted Analysis of Immunopeptidomics Data. In: Methods in Molecular Biology. Springer US, 2024 more…
