Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning.

ABSTRACT: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragmentation mechanisms in SIMS are complex because different sputtering and ionization processes can simultaneously occur. Therefore, a prediction system that can identify materials in a sample is required. A novel prediction system for peptides based on ToF-SIMS and amino-acid-based teaching information (labels) for supervised machine learning was developed. To develop the prediction system for general organic materials, the annotation of materials is crucial to creating effective labels for supervised learning. Peptides are composed of 20 amino a

SUBMITTER: Aoyagi S 

PROVIDER: S-EPMC11623169 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature