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Journal of Medical Signals and Sensors، جلد ۱۲، شماره ۲، صفحات ۱۲۲-۱۲۶
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چکیده فارسی مقاله
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عنوان انگلیسی Using classification and K-means methods to predict breast cancer recurrence in gene expression data
چکیده انگلیسی مقاله Background: Breast cancer is a type of cancer that starts in the breast tissue and affects about 10% of women at different stages of their lives. In this study, we applied a new method to predict recurrence in biological networks made from gene expression data. Method: The method includes the steps such as data collection, clustering, determining differentiating genes, and classification. The eight techniques consist of random forest, support vector machine and neural network, randomforest + k-means, hidden markov model, joint mutual information, neural network + k-means and suportvector machine + k-menas were implemented on 12172 genes and 200 samples. Results: Thirty genes were considered as differentiating genes which used for the classification. The results showed that random forest + k-means get better performance than other techniques. The two techniques including neural network + k-means and random forest + k-means performed better than other techniques in identifying high risk cases. Conclusion: Thirty of 12,172 genes are considered for classification that the use of clustering has improved the classification techniques performance.
کلیدواژه‌های انگلیسی مقاله Classification, gene, K-means
نویسندگان مقاله | Mohammadreza Sehhati
Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences; Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran


| Mohammad Amin Tabatabaiefar
Department of Hematology-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran


| Ali Haji Gholami
Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran


| Mohammad Sattari


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/613
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Original Articles
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