ProtAIomics
Researchers
Alessio Giuffrida
Viktoria Dorfer
Duration
Research Areas
Partners
University of Applied Sciences Upper Austria, Hagenberg Campus, RG Bioinformatics
Centre for Genomic Regulation (CRG)
Technical University of Munich (TUM)
University Hospital Heidelberg (UKHD)
Wellcome Sanger Institute GENOME RESEARCH LIMITED (SANGER)
Institute for Research in Biomedicine (IRB)
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE (UCAM)
EUROPEAN MOLECULAR BIOLOGY LABORATORY (EMBL-EBI)
Technical University of Denmark (DTU)
Universitat Pompeu Fabra (UPF)
Centre National de la Recherche Scientifique (CNRS-IPBS)
Institute of Molecular Biology (IMB)
Tampere University (TAU)
KTH Royal Institute of Technology
Institute of Microbiology of the Czech Academy of Sciences
ETH Zurich (ETHZ)
ProtAIomics is a Horizon Europe MSCA Doctoral Network that brings together 15 beneficiaries and 9 associated partners across academia, biotech and big-pharma to train 16 doctoral candidates at the interface of artificial intelligence and mass-spectrometry proteomics. By combining expertise in machine learning, structural biology and systems modelling, the network tackles the entire proteomics value chain—from data acquisition to actionable biomedical insights—while equipping a new generation of researchers with interdisciplinary, industry-relevant skills.
ProtAIomics is built around a simple but powerful value chain: turn raw mass-spectrometry data into reliable information, convert that information into deep biological knowledge, and channel the resulting insight into actionable solutions for medicine and biotechnology. Achieving this requires three, mutually reinforcing pillars, (1) pushing the frontier of AI-powered data acquisition and analysis, (2) training sixteen doctoral candidates through a rich mix of academic and industrial secondments, and (3) embedding open science, ethical AI and patient engagement at every step. These goals create the foundation for next-generation proteomics technologies and data-driven health innovations.
At our research group we are working on “Enhancing Spectrum Clarity: A Neural Network Approach for Signal-Noise Discrimination in DIA MS2 Data”: The goal is to harness state-of-the-art neural network architectures to improve the distinction between signal and noise peaks in MS2 spectra. Develop a robust training pipeline using DIA data to accurately classify MS2 peaks as either signals or noise. Enhance spectrum cleaning, enabling more efficient peptide identification by reducing noise from diverse sources.
