Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e.g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as stringbased features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted featur...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Wiebke Timm, Alexandra Scherbart, Sebastian Böcker, Oliver Kohlbacher, Tim W. Nattkemper
• Keywords: Absolute Quantification, BMCBI 2008, Peak Intensity, Peptides