Feature extraction and analysis of prostate cancer proteomic mass spectra for biomarker discovery
Νικηφορίδης, Γεώργιος Σ.
Κάβουρας, Διονύσης Α.
Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.
Early detection of cancer is a critical issue for
improving patient survival rates. Recent progress in mass
spectrometry has shown the promising potential of biomarker
discovery in the diagnosis of diseases especially in early stages.
In the present study, an alternative approach to feature
extraction from mass spectrometry data of prostate cancer is
proposed that results in the definition of different biomarkers.
The latter provide information rich features that improve the
performance of an MLP classifier in differentiating among
datasets with different PSA levels of prostate cancer and with
no evidence of disease. Prostate cancer dataset was collected
from the National Cancer Institute Clinical Proteomics
Database. The overall accuracy, in correctly classifying 63
spectra with no evidence of disease (PSA<1) and 69 spectra
with prostate cancer (PSA≥4), was 95%. Furthermore 93%
was the classification overall accuracy in discriminating 26
spectra of prostate cancer with (4 PSA<10) from 43 spectra
of prostate cancer with (PSA>10). The high accuracies obtained
by the proposed method might lead to informative biomarkers
for early stage of prostate cancer diagnosis.
Technological Educational Institute of Athens
International Special Topic Conference on Information Technology in Biomedicine