An Analysis of Gene Expression Variations in Lymphoma, Using a Fuzzy Classification Model

• Main Authors: Zahra Roozbahani, Jalal Rezaei Noor, Mansoureh Yari Eili, Ali Katanforoush
• Format: Article
• Language: English
• Published: Shiraz University of Medical Sciences 2017-01-01
• Series: Journal of Health Management & Informatics
• Subjects: Lymphoma Cancer; Cancer Diagnosis; Microarray; Gen Expression; Fuzzy Classifier
• Online Access: http://jhmi.sums.ac.ir/index.php/JHMI/article/view/318/105
• ISSN: 2322-1097; 2423-5857

Introduction: Cancer is a major cause of mortality in the modern world, and one of the most important health problems in societies. During recent years, research on cancer as a system biology disease is focused on molecular differences between cancer cells and healthy cells. Most of the proposed methods for classifying cancer using gene expression data act as black boxes and lack biological interpretability. The goal of this study is to design an interpretable fuzzy model for classifying gene expression data of Lymphoma cancer. Method: In this research, the investigated microarray contained 45 samples of lymphoma. Total number of genes was 4026 samples. At first, we offer a hybrid approach to reduce the data dimension for detecting genes involved in lymphoma cancer. In lymphoma microarray, six out of 4029 genes were selected. Then, a fuzzy interpretable classifier was presented for classification of data. Fuzzy inference was performed using two rules which had the highest scores. Weka3.6.9 software was used to reduce the features and the fuzzy classifier model was implemented in MATLAB R2010a. Results of this study were assessed by two measures of accuracy and precision. Results: In pre-processing stage, in order to classify gene expression data of Lymphoma, six out of 4026 genes were identified as cancer-causing genes, and then the fuzzy classifier model was applied on the obtained data. The accuracy of the results of classification was 96 percent using 10 rules with the highest scores and that using 2 rules with the highest scores was about 98 percent. Conclusion: In the proposed approach, for the first time, a fully fuzzy method named a minimal rule fuzzy classification (MRFC) was introduced for extracting fuzzy rules with biological interpretability and meaning extraction from gene expression data. Among the most outstanding features of this method is the ability of extracting a small set of rules to interpret effective gene expression in cancer patients. Another result of this approach is successfully addressing the problem of disproportion between the number of samples and genes in microarrays with the proposed Filter-Wrapper Feature Selection method (FWFS).