Volume 17, Issue 2 (7-2022)                   MGj 2022, 17(2): 137-147 | Back to browse issues page

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Hajieskandar A, Khalilian M, Mohammadzadeh J, Najafi A. Detecting Acute Lymphocytic and Myeloid Leukaemia by Selecting Effective Microarray and Data-Mining Algorithms. MGj 2022; 17 (2) :137-147
URL: http://mg.genetics.ir/article-1-1732-en.html
Karaj Branch, Islamic Azad University
Abstract:   (759 Views)
Cancer is one of the leading causes of death in the world. In most cases, it can be treated if the disease is detected earlier. One way to diagnose cancer is to use microarray data, which, unlike imaging, does not contain harmful rays to humans. Microarrays have many genes that complicate and take time to analyse, so selecting useful genes is one of the key steps in diagnosing the disease. The proposed method in this paper has two main phases, the first of which is the selection of effective genes. In the next phase, the disease is diagnosed from the selected genes by the first phase. In the past, many algorithms such as Ridge have been proposed for this purpose, which according to the results of experiments, the accuracy of the method proposed in this paper is superior to them. In this paper, leukaemia and colon cancer datasets are used as input and evaluation of the proposed method. The precision of the proposed method for locating genes and diagnosing leukaemia and colorectal cancer is 97.62% and 92.31%, respectively. The results obtained from this method compared to other existing methods in terms of precision in the leukaemia dataset 2.39% and in the colorectal cancer dataset 5.62% improved; It is also easier to access and less expensive than biological experiments.
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Type of Study: Applicable | Subject: Subject 03
Received: 2021/09/18 | Accepted: 2022/04/13 | Published: 2022/07/9

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