Work

A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models

Year: 2024

Type: data-paper

Abstract: Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from nine different species. The dataset is based on a previously described benchmark but has been re-processed to ensure consistent data quality and enforce separation of training and test peptides.

Source: Scientific Data

DOI: 10.1038/s41597-024-04068-4

Authors: Bo Wen, William Stafford Noble

Institution: University of Washington

Language: English

Topic: Advanced Proteomics Techniques and Applications

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,290

Funder: National Science Foundation