Biomarker Discovery by Imperialist Competitive Algorithm in Mass Spectrometry Data for Ovarian Cancer Prediction
عنوان مقاله: Biomarker Discovery by Imperialist Competitive Algorithm in Mass Spectrometry Data for Ovarian Cancer Prediction
شناسه ملی مقاله: JR_JMSI-11-2_005
منتشر شده در در سال 1400
شناسه ملی مقاله: JR_JMSI-11-2_005
منتشر شده در در سال 1400
مشخصات نویسندگان مقاله:
Shiva Pirhadi - Department of Biomedical Engineering, Tehran Science and Research Branch, Islamic Azad University
Keivan Maghooli - Department of Biomedical Engineering, Tehran Science and Research Branch, Islamic Azad University
Nilofar Yousefi Moteghaed - Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences
Masoud Garshasbi - Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran
خلاصه مقاله:
Shiva Pirhadi - Department of Biomedical Engineering, Tehran Science and Research Branch, Islamic Azad University
Keivan Maghooli - Department of Biomedical Engineering, Tehran Science and Research Branch, Islamic Azad University
Nilofar Yousefi Moteghaed - Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences
Masoud Garshasbi - Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran
Background: Mass spectrometry is a method for identifying proteins and could be used for
distinguishing between proteins in healthy and nonhealthy samples. This study was conducted using
mass spectrometry data of ovarian cancer with high resolution. Usually, diagnostic and monitoring
tests are done according to sensitivity and specificity rates; thus, the aim of this study is to
compare mass spectrometry of healthy and cancerous samples in order to find a set of biomarkers
or indicators with a reasonable sensitivity and specificity rates. Methods: Therefore, combination
methods were used for choosing the optimum feature set as t-test, entropy, Bhattacharya, and an
imperialist competitive algorithm with K-nearest neighbors classifier. The resulting feature from each
method was feed to the C۵ decision tree with ۱۰-fold cross-validation to classify data. Results: The
most important variables using this method were identified and a set of rules were extracted. Similar
to most frequent features, repetitive patterns were not obtained; the generalized rule induction
method was used to identify the repetitive patterns. Conclusion: Finally, the resulting features were
introduced as biomarkers and compared with other studies. It was found that the resulting features
were very similar to other studies. In the case of the classifier, higher sensitivity and specificity rates
with a lower number of features were achieved when compared with other studies.
کلمات کلیدی: Biomarker discovery, imperialist competitive algorithm, mass spectrometry high‑throughput proteomics data, ovarian cancer
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1700127/