Dimaggio, P.A. and Floudas, C.A. (2007) De Novo Peptide Identification via Tandem Mass Spectrometry and Integer Linear Optimization. Analytical Chemistry, 79, 1433-1446.
https://doi.org/10.1021/ac0618425

has been cited by the following article:

• TITLE: Classification of B and Y Ions in Peptide MS/MS Spectra Based on Machine Learning

  AUTHORS: Xinming Li

  KEYWORDS: Ion-Type Classification, Machine Learning, LightGBM, Proteomics, Tandem Mass Spectrometry

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.3, March 31, 2023

  ABSTRACT: In proteomics, b and y ions serve as the backbone ions for peptide sequencing in tandem mass spectrometry. Leveraging the existing ion recognition and separation methods, this article proposes a novel ion classification approach that combines machine learning with graph theory. By incorporating graph features, the method achieves higher accuracy and efficiency in ion type recognition, with the graph features playing a critical role in the classification process. Specifically, the method achieves a recall rate of nearly 90% for b and y ions, demonstrating its effectiveness in pre-processing de novo sequencing and improving its accuracy. The proposed method represents advancement in ion classification and has the potential to improve the accuracy and efficiency of de novo sequencing.