The data‐independent acquisition mass spectrometry (DIA‐MS) has rapidly evolved as a
powerful alternative for highly reproducible proteome profiling with a unique strength of …

Proteomics, the study of all the proteins in biological systems, is becoming a data‐rich
science. Protein sequences and structures are comprehensively catalogued in online …

The inclusion of peptide retention time prediction promises to remove peptide identification
ambiguity in complex liquid chromatography–mass spectrometry identification workflows …

Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human
proteins. However, experimental and computational challenges restrict progress in the field …

Metaproteomics has matured into a powerful tool to assess functional interactions in
microbial communities. While many metaproteomic workflows are available, the impact of …

Phosphorylation is one of the quickest post-translational modifications that controls
downstream signaling pathways regulating processes like cell proliferation, survival, and …

Interest in the use of machine learning for peptide fragmentation spectrum prediction has
been strongly on the rise over the past years, especially for applications in challenging …

Imbalanced datasets affect the performance of machine learning algorithms adversely. To
cope with this problem, several resampling methods have been developed recently. In this …

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based
proteomics was unfolded with the introduction of dozens of novel instruments that …

Recently pipelines of machine learning-based classification models have become important
to codify, orchestrate, and automate the workflow to produce an effective machine learning …