Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk.

ABSTRACT:

Purpose

Because age-related macular degeneration (AMD) is a progressive disorder and advanced AMD is currently hard to cure, an accurate and informative prediction of a person's AMD risk using genetic information is desirable for early diagnosis and potential individualized clinical management. The objective of this study was to develop and validate novel prediction models for AMD risk using large genome-wide association studies datasets with different machine learning approaches.

Methods

Genotype data from 32,215 Caucasian individuals with age of ≥50 years from the International AMD Genomics Consortium in dbGaP were used to establish and test prediction models for AMD risk. Four different machine learning approaches-neural network, lasso regression, support vector machine,

SUBMITTER: Yan Q 

PROVIDER: S-EPMC7900884 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature