A data-fusion approach to identifying developmental dyslexia from multi-omics datasets.

ABSTRACT: This exploratory study tested and validated the use of data fusion and machine learning techniques to probe high-throughput omics and clinical data with a goal of exploring the etiology of developmental dyslexia. Developmental dyslexia is the leading learning disability in school aged children affecting roughly 5-10% of the US population. The complex biological and neurological phenotype of this life altering disability complicates its diagnosis. Phenome, exome, and metabolome data was collected allowing us to fully explore this system from a behavioral, cellular, and molecular point of view. This study provides a proof of concept showing that data fusion and ensemble learning techniques can outperform traditional machine learning techniques when provided small and complex multi-omics and

SUBMITTER: Carrion J 

PROVIDER: S-EPMC10002702 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature