Department of Software Engineering, Istanbul Aydin University, Istanbul, Turkey.
Department of Computer Engineering, Istanbul Gelisim University, Istanbul, Turkey.
Anal Biochem. 2021 Aug 15;627:114242. doi: 10.1016/j.ab.2021.114242. Epub 2021 May 8.
This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: binary dragonfly algorithm (BDF) and binary black hole algorithm (BBHA). This hybridization aims to identify a limited and stable set of discriminative genes without sacrificing classification accuracy, whereas most current methods have encountered challenges in extracting disease-related information from a vast amount of redundant genes. The proposed approach first applies the minimum redundancy maximum relevancy (MRMR) filter method to reduce the dimensionality of feature space and then utilizes the suggested hybrid DBH algorithm to determine a smaller set of significant genes. The proposed approach was evaluated on eight benchmark gene expression datasets, and then, was compared against the latest state-of-art techniques to demonstrate algorithm efficiency. The comparative study shows that the proposed approach achieves a significant improvement as compared with existing methods in terms of classification accuracy and the number of selected genes. Moreover, the performance of the suggested method was examined on real RNA-Seq coronavirus-related gene expression data of asthmatic patients for selecting the most significant genes in order to improve the discriminative accuracy of angiotensin-converting enzyme 2 (ACE2). ACE2, as a coronavirus receptor, is a biomarker that helps to classify infected patients from uninfected in order to identify subgroups at risk for COVID-19. The result denotes that the suggested MRMR-DBH approach represents a very promising framework for finding a new combination of most discriminative genes with high classification accuracy.
本文提出了一种新的混合方法（DBH），用于解决基因选择问题，该方法结合了两种现有元启发式算法的优势：二进制蜻蜓算法（BDF）和二进制黑洞算法（BBHA）。这种混合方法旨在在不牺牲分类准确性的情况下，确定一个有限且稳定的有区别基因集，而大多数当前方法在从大量冗余基因中提取疾病相关信息方面遇到了挑战。该方法首先应用最小冗余最大相关性（MRMR）过滤方法来降低特征空间的维数，然后利用建议的混合 DBH 算法来确定一组更小的显著基因。该方法在八个基准基因表达数据集上进行了评估，然后与最新的最先进技术进行了比较，以证明算法的效率。比较研究表明，与现有方法相比，该方法在分类准确性和选择的基因数量方面都有显著的提高。此外，还在哮喘患者的真实 RNA-Seq 冠状病毒相关基因表达数据上检查了所建议方法的性能，以选择最显著的基因，从而提高血管紧张素转换酶 2（ACE2）的判别准确性。ACE2 作为冠状病毒受体，是一种有助于将感染患者与未感染患者区分开来的生物标志物，以识别 COVID-19 高危亚组。结果表明，所提出的 MRMR-DBH 方法代表了一种很有前途的框架，可以找到具有高分类准确性的最具区别性基因的新组合。
