Pirmoradi Saeed, Teshnehlab Mohammad, Zarghami Nosratollah, Sharifi Arash
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Systems and Control Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Comput Methods Programs Biomed. 2021 Jul;206:106132. doi: 10.1016/j.cmpb.2021.106132. Epub 2021 Apr 27.
Kidney cancer is a dangerous disease affecting many patients all over the world. Early-stage diagnosis and correct identification of kidney cancer subtypes play an essential role in the patient's survival; therefore, its subtypes diagnosis and classification are the main challenges in kidney cancer treatment. Medical studies have proved that miRNA dysregulation can increase the risk of cancer. Thus, in this paper, we propose a new machine learning approach for significant miRNAs identification and kidney cancer subtype classification to design an automatic diagnostic tool. The proposed method contains two main steps: feature selection and classification. First, we apply the feature selection algorithm to choose the candidate miRNAs for each subtype. The feature selection algorithm utilizes the AMGM measure to select significant miRNAs with high discriminant power. Next, the candidate miRNAs are fed to a classifier to evaluate the candidate features. In the classification step, the proposed self-organizing deep neuro-fuzzy system is employed to classify kidney cancer subgroups. The new deep neuro-fuzzy system consists of a deep structure in the rule layer and novel architecture in the fuzzifier layer. The proposed self-organizing deep neuro-fuzzy system can help us to overcome the main obstacles in the field of neuro-fuzzy system applications, such as the curse of dimensionality. The goal of this paper is to illustrate that the neuro-fuzzy system can very useful in high dimensional data, such as genomics data, using the proposed deep neuro-fuzzy system. The obtained results illustrated that our proposed method has succeeded in classifying kidney cancer subtypes with high accuracy based on the selected miRNAs.
肾癌是一种影响全球众多患者的危险疾病。早期诊断和正确识别肾癌亚型对患者的生存起着至关重要的作用；因此，其亚型诊断和分类是肾癌治疗中的主要挑战。医学研究证明，miRNA失调会增加患癌风险。因此，在本文中，我们提出了一种新的机器学习方法，用于识别重要的miRNA和进行肾癌亚型分类，以设计一种自动诊断工具。所提出的方法包含两个主要步骤：特征选择和分类。首先，我们应用特征选择算法为每个亚型选择候选miRNA。该特征选择算法利用AMGM度量来选择具有高判别力的重要miRNA。接下来，将候选miRNA输入到分类器中以评估候选特征。在分类步骤中，采用所提出的自组织深度神经模糊系统对肾癌亚组进行分类。新的深度神经模糊系统在规则层具有深度结构，在模糊化层具有新颖的架构。所提出的自组织深度神经模糊系统可以帮助我们克服神经模糊系统应用领域中的主要障碍，如维数灾难。本文的目的是说明使用所提出的深度神经模糊系统，神经模糊系统在高维数据（如基因组数据）中非常有用。所得结果表明，我们提出的方法基于所选的miRNA成功地对肾癌亚型进行了高精度分类。
