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

Area of Science:

• Genomics and Bioinformatics
• Neuroscience
• Computational Biology

Background:

• Complex genetic disorders affect the brain, necessitating advanced analysis techniques.
• Genetic variation analysis, including DNA microarrays, is key to understanding these disorders.
• Machine learning offers powerful tools for classifying complex brain diseases.

Purpose of the Study:

• To review state-of-the-art genetic variation analysis for discovering complex genes in brain disorders.
• To focus on machine learning methods for brain disease classification using single nucleotide polymorphisms (SNPs).
• To discuss applications in autism spectrum disorder, brain cancer, and Alzheimer's disease (AD).

Main Methods:

• Literature survey of academic databases.
• Inclusion/exclusion criteria for article selection.
• Detailed presentation of machine learning methods for disease classification.

Main Results:

• Machine learning, especially using SNPs, is widely applied to genetic variation problems in complex diseases.
• SNPs provide high-resolution genomic fingerprinting for tracking disease genes.

Conclusions:

• Despite advances, determining causative mutations for genetic brain disorders remains challenging.
• Early detection of gene variations in brain disorders is essential for improved diagnosis and treatment.
• Implementing genetic analysis in clinical practice faces significant hurdles.