TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.

ABSTRACT: One of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods are difficult to analyze the corresponding high-dimensional massive data or SNPs. Recently, machine learning methods have become more popular in high-dimensional genetic data analysis for their fast computation speed. However, most of machine learning methods have several drawbacks, such as poor generalization ability, over-fitting, unsatisfactory classification and low detection accuracy. This study proposed a two-stage algorithm based on least angle regression and random forest (TSLRF), which firstly considered the control of population struct

SUBMITTER: Sun J 

PROVIDER: S-EPMC6889171 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature