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ICANN
2007
Springer

Neural Network Approach for Mass Spectrometry Prediction by Peptide Prototyping

13 years 7 months ago
Neural Network Approach for Mass Spectrometry Prediction by Peptide Prototyping
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 -Support Vector Regression and show how the LLM learning architecture provides a basis for peptide feature profiling and visualisation.
Alexandra Scherbart, Wiebke Timm, Sebastian Bö
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where ICANN
Authors Alexandra Scherbart, Wiebke Timm, Sebastian Böcker, Tim W. Nattkemper
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