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ORIGINAL ARTICLE
Year : 2018  |  Volume : 8  |  Issue : 3  |  Page : 161-169

An optimized framework for cancer prediction using immunosignature


1 Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
2 Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
3 Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran

Correspondence Address:
Alireza Vard
Department of Biomedical Engineering, School of Advanced Technologies in Medicine and Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_2_18

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Background: Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited. In this article, we introduce a robust method for predicting eight types of cancers including astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced pancreatic cancer, and Ewing sarcoma. Methods: In the proposed scheme, at first, the database is normalized with a dictionary of normalization methods that are combined with particle swarm optimization (PSO) for selecting the best normalization method for each feature. Then, statistical feature selection methods are used to separate discriminative features and they were further improved by PSO with appropriate weights as the inputs of the classification system. Finally, the support vector machines, decision tree, and multilayer perceptron neural network were used as classifiers. Results: The performance of the hybrid predictor was assessed using the holdout method. According to this method, the minimum sensitivity, specificity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1, 90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classification that are used in our algorithm. Conclusion: The proposed algorithm considers all the circumstances and works with each feature in its special way. Thus, the proposed algorithm can be used as a promising framework for cancer prediction with immunosignature.


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