Molecular cancer classification method on microarrays gene expression data using hybrid deep neural network and grey wol
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ORIGINAL RESEARCH
Molecular cancer classification method on microarrays gene expression data using hybrid deep neural network and grey wolf algorithm AliReza Hajieskandar1 · Javad Mohammadzadeh1 · Majid Khalilian1 · Ali Najafi2 Received: 23 December 2019 / Accepted: 14 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Gene selection methods are critical in cancer classification, which depends on the expression of a small number of biomarker genes, which have been a significant issue of enormous recent studies. Microarray technology allows generating tumors gene expression datasets. Cancer classification based on these datasets commonly has a kind of small sample size against the number of genes involved and includes multiclass categories. In this paper, grey wolf algorithm was used for extracting notable features in the pre-processing stage, and deep neural network (DNN) was used as deep learning for improving the accuracy degree of cancer detection from three datasets, i.e., STAD (Stomach adenocarcinoma), LUAD (lung adenocarcinoma) and BRCA (breast invasive carcinoma). The proposed method achieved the highest accuracy for these three datasets. The proposed method was able to achieve accuracy close to 100. Furthermore, the proposed method was compared with linear support vector machine classification, RBF, the nearest neighbor, linear regression, one vs. all, Naive Bayes, and decision tree algorithms. The proposed method had 0.57 improvement on the LUAD dataset, 1.11 optimization on the STAD dataset, and 0.78 development on the BRCA dataset. Keywords Cancer classification · DNA Microarray · Deep neural networks · Grey wolf algorithm · Deep learning
1 Introduction Cancer is a multifactorial and complex disorder, mostly caused by acquired mutations and epigenetic alterations that affect gene expression. Accordingly, most of the cancer investigations focus on the identification of genetic biomarkers that can be used to precisely diagnose and effective * Javad Mohammadzadeh [email protected] AliReza Hajieskandar [email protected] Majid Khalilian [email protected] Ali Najafi [email protected] 1
Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
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treatment (Butterfield et al. 2017; Knudson 2000). 90% of human cancers have an epithelial origin, which shows aneuploidy, deletions, duplications, and genetic instability. These complexities probably explain the clinical diversities of similar tumor tissues and the need for a comprehensive understanding of the genetic changes in tumors (Chen et al. 2005; Gray and Collins 2000). The initial human genome sequence has led to the identification of genetic complexities of the common cancers using advanced technologies. Now, there are high-throughput technologies to identify all the cancer abnormalities at DNA, RNA, and protein levels. The gene e
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