Neural Network Approach for Mass Spectrometry Prediction by Peptide Prototyping

Abstract

Abstract. In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type for peptide prototyping and learning locally tuned regression functions for peak intensity prediction in MALDI-TOF mass spectra. We obtain results comparable to those obtained by ν-Support Vector Regression and show how the LLM learning architecture provides a basis for peptide feature profiling and visualisation.