A novel ensemble strategy for classification of prostate cancer protein mass spectra.
Protein mass spectra pattern recognition is a new forum in which many machine learning algorithms have been conducted to enhance the chance of early cancer diagnosis. The high-dimensionality-small-sample (HDSS) problem of cancer proteomic datasets still requires more sophisticated approaches to improve the classification accuracy. In this study we present a simple ensemble strategy based on measuring the generalizing capability of different subsets of training data and apply it in making final decision. Using a limited number of biomarkers along with 5 classification algorithms, the proposed method achieved a promising performance over a well-known prostate cancer mass spectroscopy dataset.
Assareh
Amin
A
Biomedical Engineering Faculty of Amirkabir University of Technology, Tehran, Iran.
Moradi
Mohammad Hassan
MH
Esmaeili
Vahid
V
D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000097:blood*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D013058:Mass Spectrometry / Q000379:methods*; D009363:Neoplasm Proteins / Q000097:blood*; D010363:Pattern Recognition, Automated / Q000379:methods; D010449:Peptide Mapping / Q000379:methods; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000097:blood* / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D017430:Prostate-Specific Antigen