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SOM-based Peptide Prototyping for Mass Spectrometry Peak Intensity Prediction
Scherbart, Alexandra ; Timm, Wiebke ; Böcker, Sebastian ; Nattkemper, Tim W.
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 based on Self-Organizing Maps (SOMs) 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 nu-Support Vector Regression and show how the SOM learning architecture provides a basis for peptide feature profiling and visualisation.
||Peak Intensity Prediction, Self-Organizing Map, Local Linear Map, Maldi-MS
||Faculty of Technology, Research Groups in Informatics
||Data processing, computer science, computer systems
Scherbart, Alexandra ; Timm, Wiebke ; Böcker, Sebastian ; Nattkemper, Tim W. (2007) SOM-based Peptide Prototyping for Mass Spectrometry Peak Intensity Prediction.