Background Spectral library searching is currently the most common approach for
compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid …

Abstract Machine learning (ML) and deep learning (DL) models for peptide property
prediction such as Prosit have enabled the creation of high quality in silico reference …

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

Spectrum library searching is a powerful alternative to database searching for data
dependent acquisition experiments, but has been historically limited to identifying previously …

Mass spectral libraries are collections of reference spectra, usually associated with specific
analytes from which the spectra were generated, that are used for further downstream …

Sample multiplexed quantitative proteomics assays have proved to be a highly versatile
means to assay molecular phenotypes. Yet, stochastic precursor selection and precursor …

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

In protein‐RNA cross‐linking mass spectrometry, UV or chemical cross‐linking introduces
stable bonds between amino acids and nucleic acids in protein‐RNA complexes that are …

Recent developments in machine-learning (ML) and deep-learning (DL) have immense
potential for applications in proteomics, such as generating spectral libraries, improving …

The identification of peptides is a cornerstone of mass spectrometry-based proteomics.
Spectral library-based algorithms are well-established methods to enhance the identification …