Biomarker discovery by imperialist competitive algorithm in mass spectrometry data for ovarian cancer prediction

• Published in: Journal of Medical Signals and Sensors
• Main Authors: Shiva Pirhadi, Keivan Maghooli, Niloofar Yousefi Moteghaed, Masoud Garshasbi, Seyed Jalaleddin Mousavirad
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2021-01-01
• Subjects: biomarker discovery; imperialist competitive algorithm; mass spectrometry high-throughput proteomics data; ovarian cancer
• Online Access: http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2021;volume=11;issue=2;spage=108;epage=119;aulast=Pirhadi

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.