TY  - BOOK
AB  - 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.
DA  - 2007
DO  - 10.2390/biecoll-wsom2007-157
LA  - eng
PY  - 2007
TI  - SOM-based Peptide Prototyping for Mass Spectrometry Peak Intensity Prediction
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27140459
Y2  - 2024-11-22T04:58:03
ER  -