Ahmed Hala, Alarabi Louai, El-Sappagh Shaker, Soliman Hassan, Elmogy Mohammed
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia.
PeerJ Comput Sci. 2021 Sep 20;7:e697. doi: 10.7717/peerj-cs.697. eCollection 2021.
This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.
We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.
It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.
Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.
本文对用于发现与脑部遗传疾病相关的复杂基因的最新基因变异分析进行了深入综述。我们首先介绍复杂脑部疾病的遗传分析、基因变异和DNA微阵列。然后，综述聚焦于用于复杂脑部疾病分类的现有机器学习方法。其中，我们讨论了各种数据集、预处理、特征选择与提取以及分类策略。特别地，我们着重研究单核苷酸多态性（SNP），其为追踪疾病基因的基因组指纹识别提供了最高分辨率。随后，该研究概述了某些特定疾病的应用，包括自闭症谱系障碍、脑癌和阿尔茨海默病（AD）。该研究认为，尽管近期在遗传疾病的分析和治疗方面取得了重大进展，但在阐明致病突变方面仍存在相当大的挑战，尤其是从在临床实践中实施遗传分析的角度来看。综述最后对基因变异分析在复杂脑部疾病识别中的适用性进行了批判性讨论，突出了未来的挑战。
我们采用文献调查方法从学术数据库获取数据。定义了纳入和排除标准。文章的选择分三个阶段进行。此外，在每个阶段更详细地介绍了用于疾病分类的机器学习的主要方法。
结果表明，基于SNP的机器学习被广泛用于解决与基因相关的复杂疾病的基因变异问题。
尽管在过去二十年中遗传疾病的诊断和治疗取得了重大进展，但仍有很大比例的致病突变无法确定，最终的基因诊断仍然难以捉摸。因此，我们需要在疾病早期阶段检测与脑部疾病相关的基因变异。
