In recent years machine learning has made extensive progress in modeling many aspects of
mass spectrometry data. We brought together proteomics data generators, repository …

In recent years, the rapid growth of biological data has increased interest in using
bioinformatics to analyze and interpret this data. Proteomics, which studies the structure …

Abstract Machine learning and in particular deep learning (DL) are increasingly important in
mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention …

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich
history with several modern MS-based applications using statistical and chemometric …

In computational proteomics, machine learning (ML) has emerged as a vital tool for
enhancing data analysis. Despite significant advancements, the diversity of ML model …

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and
vaccine development. However, identifying immunopeptides remains challenging due to …

Peptide identification in mass spectrometry-based proteomics is crucial for understanding
protein function and dynamics. Traditional database search methods, though widely used …

The unbounded permutations of biological molecules, including proteins and their
constituent peptides, present a dilemma in identifying the components of complex …

Proteomics is the interdisciplinary field focusing on the large-scale study of proteins.
Proteins essentially organize and execute all functions within organisms. Today, the bottom …

Targeted proteomics has been playing an increasingly important role in hypothesis-driven
protein research and clinical biomarker discovery. We previously created a workflow …