摘要
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.
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.
作者
Xinming Li
Xinming Li(School of Computer Science and Technology, Shandong University of Technology, Zibo, China)
