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

ABSTRACT:

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,

SUBMITTER: Ahmed H 

PROVIDER: S-EPMC8459785 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature