Nazari Elham, Farzin Amir Hossein, Aghemiri Mehran, Avan Amir, Tara Mahmood, Tabesh Hamed
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Computer Engineering, Khayyam University, Mashhad, Iran.
J Med Life. 2020 Jul-Sep;13(3):382-387. doi: 10.25122/jml-2019-0090.
By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widely used in cancer diagnosis and screening since it is able to measure gene expression levels. Analyzing them by using common statistical methods is not suitable because of the high gene expression data dimensions. So, this study aims to use new techniques to diagnose acute myeloid leukemia. In this study, the leukemia microarray gene data, contenting 22283 genes, was extracted from the Gene Expression Omnibus repository. Initial preprocessing was applied by using a normalization test and principal component analysis in Python. Then DNNs neural network designed and implemented to the data and finally results cross-validated by classifiers. The normalization test was significant (P>0.05) and the results show the PCA gene segregation potential and independence of cancer and healthy cells. The results accuracy for single-layer neural network and DNNs deep learning network with three hidden layers are 63.33 and 96.67, respectively. Using new methods such as deep learning can improve diagnosis accuracy and performance compared to the old methods. It is recommended to use these methods in cancer diagnosis and effective gene selection in various types of cancer.
随着生活方式的改变和癌症发病率的上升，准确诊断成为一项重要的医疗行为。如今，DNA微阵列因其能够测量基因表达水平而被广泛应用于癌症诊断和筛查。由于基因表达数据维度高，使用普通统计方法对其进行分析并不合适。因此，本研究旨在使用新技术诊断急性髓系白血病。在本研究中，从基因表达综合数据库中提取了包含22283个基因的白血病微阵列基因数据。在Python中使用归一化测试和主成分分析进行初始预处理。然后对数据设计并实现深度神经网络（DNNs），最后通过分类器对结果进行交叉验证。归一化测试具有显著性（P>0.05），结果显示了主成分分析（PCA）对基因的分离潜力以及癌症细胞和健康细胞的独立性。单层神经网络和具有三个隐藏层的深度神经网络（DNNs）深度学习网络的结果准确率分别为63.33和96.67。与旧方法相比，使用深度学习等新方法可以提高诊断准确性和性能。建议在癌症诊断和各类癌症的有效基因选择中使用这些方法。
