Peak intensity prediction in mass spectra using machine learning method
Mass spectrometry (MS) is an indispensable technique for the fast analysis of proteins and peptides in complex biological samples. One key problem with the quantitative mass spectrometric analysis of peptides and proteins, however, is the fact that the sensitivity of MS instruments is peptide-dependent, leading to an unclear relationship between the observed peak intensity and the peptide concentration in the sample. Various labeling techniques have been developed to circumvent this problem, but are very expensive and time-consuming. A reliable prediction of peptide-specific sensitivies could provide a peptide-specific correction factor, which would be valuable for label-free absolute quantitation.
This package of scripts contains tools that are organized in a pipeline. These have been used in the context of the phd project "Peak intensity prediction in mass spectra using machine learning methods" to extract peptide peaks from MALDI spectra, normalize intensities, generate features for machine learning, as well as train and test the prediction. You can view the abstract or downloaded the thesis from Bielefeld University. This package is provided as is, no guarantees are given for any of its functions. There is some documentation included with it which should allow a computer scientist to use or adapt the included scripts and software as seen fit. For further questions, feel free to contact the author.