A self-organizing deep neuro-fuzzy system approach for classification of kidney cancer subtypes using miRNA genomics data. (July 2021)

• Record Type: Journal Article
• Authors: Pirmoradi, Saeed; Teshnehlab, Mohammad; Zarghami, Nosratollah; Sharifi, Arash
• Is Part Of: Computer methods and programs in biomedicine. Volume 206(2021)
• Publication Date: 2021-07
• Subjects: Neuro-fuzzy system -- Deep learning -- Self-organizing auto-encoder -- Kidney cancer -- miRNA -- The Cancer Genome Atlas (TCGA); Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106132
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The proposed deep neuro-fuzzy system with new topology is applicable in high dimension data, especially genomics data. The proposed deep neuro-fuzzy system includes new topology in the fuzzification layer and rule layer. The deep learning is used to create complex rules from simple rules in the rule layer. The significant miRNAs are selected, by the proposed feature selection algorithm; and kidney cancer subtypes classification is performed, by the proposed deep neuro-fuzzy system. The proposed model succeeded in classification with high accuracy. Abstract: 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. Graphical Abstract: Image, graphical abstract