Genetic Programming for Measuring Peptide Detectability

The biomarker discovery process usually produces a long list of candidates, which need to be verified. The verification of protein biomarkers from mass spectrometry data can be done through measuring the detection probability from the mass spectrometer Peptide detection. However, the limited size of the experimental data and lack of a universal quantitative method make the identification of these peptides challenging. In this paper, genetic programming GP is proposed to measure the detection of the peptides in the mass spectrometer. This is done through measuring the physicochemical chemicals of the peptides and selecting the high responding peptides. The proposed method performs both feature selection and classification, where feature selection is adopted to determine the important physicochemical properties required for the prediction. The proposed GP method is tested on two different yeast data sets with increasing complexity. It outperforms five other state-of-the-art classification algorithms. The results also show that GP outperforms two conventional feature selection methods, namely, Chi Square and Information Gain Ratio.