Discovery of Proteomics based on Machine learning

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. Index Terms – proteotypic, Support Vector Machine, Random Forest.