Developing an Early Predictive System for Identifying Genetic Biomarkers Associated to Alzheimer’s Disease Using Machine Learning Techniques

Hamid, Marwa Mostafa Abd El; Mabrouk, Mai S.; Omar, Yasser M. K.

doi:10.4015/s1016237219500406

Cited by 13 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Abd El Hamid, Mabrouk & Omar (2019) developed a method for detecting biomarkers SNPs associated with the disease with high accuracy classification, leading to early disease prediction and diagnosis. For discovering new biomarkers, ML techniques are utilized for the dis-ease.…”

Section: Literature Review On Complex Disease and Applicationsmentioning

confidence: 99%

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

Alarabi

El–Sappagh

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Background and Objectives 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. Methods 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. Results It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. Conclusions 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.

show abstract

Section: Literature Review On Complex Disease and Applicationsmentioning

confidence: 99%

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

Alarabi

El–Sappagh

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

Chaotic Chicken Swarm Optimization-Based Deep Adaptive Clustering for Alzheimer Disease Detection

Dhanusha

Kumar

Musirin

et al. 2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Multiple sclerosis: an associated single-nucleotide polymorphism study on Egyptian population

Hamid¹,

Ali

Saad

et al. 2020

Netw Model Anal Health Inform Bioinforma

Self Cite

View full text Add to dashboard Cite

Multiple Sclerosis (MS) is an autoimmune disease that severely impacts the central nervous system. Thanks to the evolutionary genetic information studied and published, researches have started to study MS from a genetic perspective. This paper provides a study of six single-nucleotide polymorphisms (SNPs)-MIR137HG (rs1625579), GAS5 (rs2067079), MIR3142HG (rs57095329), MIR146A (rs2910164), MIR155HG (rs767649) and IRAK1 (rs3027898)-and demonstrates their association with MS. This study was applied over an Egyptian dataset of 38 MS patients and 35 controls. Hence, different models were applied, Dominant, Recessive and Genotypic models along with Fisher's Exact method, Basic case-control analysis and Logistic regression analysis. This paper shows that the SNPs rs1625579, rs57095329, rs767649 and rs3027898 are associated with MS (p value < 0.05) according to all tested models except for Recessive model, that has add-in the relevance of rs1625579, rs57095329, rs2910164 and rs767649 with MS disease.

show abstract

Developing an Early Predictive System for Identifying Genetic Biomarkers Associated to Alzheimer’s Disease Using Machine Learning Techniques

Cited by 13 publications

References 27 publications

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Chaotic Chicken Swarm Optimization-Based Deep Adaptive Clustering for Alzheimer Disease Detection

Multiple sclerosis: an associated single-nucleotide polymorphism study on Egyptian population

Contact Info

Product

Resources

About