این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Medical Signals and Sensors، جلد ۱۱، شماره ۲، صفحات ۱۰۸-۱۱۹
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Biomarker Discovery by Imperialist Competitive Algorithm in Mass Spectrometry Data for Ovarian Cancer Prediction
چکیده انگلیسی مقاله 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 C5 decision tree with 10-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.
کلیدواژه‌های انگلیسی مقاله Biomarker discovery, imperialist competitive algorithm, mass spectrometry high-throughput proteomics data, ovarian cancer
نویسندگان مقاله | Shiva Pirhadi
Department of Biomedical Engineering, Tehran Science and Research Branch, Islamic Azad University


| Keivan Maghooli
Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences


| Niloofar Yousefi Moteghaed
Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran


| Masoud Garshasbi
Department of Engineering, Sabzevar University of New Technologies, Sabzevar, Iran


| Seyed Jalaleddin Mousavirad


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/573
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده Original Articles
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات