  The ultimate target of proteomics identification is to identify and quantify
the protein in the organism. Mass spectrometry (MS) based on label-free protein
quantitation has mainly focused on analysis of peptide spectral counts and ion
peak heights. Using several observed peptides (proteotypic) can identify the
origin protein. However, each peptide's possibility to be detected was severely
influenced by the peptide physicochemical properties, which confounded the
results of MS accounting. Using about a million peptide identification
generated by four different kinds of proteomic platforms, we successfully
identified >16,000 proteotypic peptides. We used machine learning
classification to derive peptide detection probabilities that are used to
predict the number of trypic peptides to be observed, which can serve to
estimate the absolutely abundance of protein with highly accuracy. We used the
data of peptides (provides by CAS lab) to derive the best model from different
kinds of methods. We first employed SVM and Random Forest classifier to
identify the proteotypic and unobserved peptides, and then searched the best
parameter for better prediction results. Considering the excellent performance
of our model, we can calculate the absolutely estimation of protein abundance.
