A data-fusion approach to identifying developmental dyslexia from multi-omics datasets
Jackson Carrion1, Rohit Nandakumar1, Xiaojian Shi1,2
1College of Health Solutions, Arizona State University, Phoenix, AZ 85004.
Biorxiv : the Preprint Server for Biology
|March 13, 2023
Summary
This study used data fusion and machine learning to explore the causes of developmental dyslexia (DD). Ensemble methods outperformed traditional techniques, identifying potential genetic biomarkers for DD.
Area of Science:
- Neuroscience
- Genetics
- Computational Biology
Background:
- Developmental dyslexia (DD) is a common learning disability affecting 5-10% of US children.
- The complex etiology of DD hinders accurate diagnosis.
- Multi-omics and clinical data offer a comprehensive approach to understanding DD.
Conclusions:
- Data fusion and ensemble learning are effective for analyzing complex multi-omics and clinical data.
- Machine learning models can aid in classifying developmental dyslexia.
- Genetic variations in the thalamus and cerebellum may play a role in DD etiology.
Keywords:
Data-fusionDevelopmental DyslexiaExplainable AIMachine LearningMixture of ExpertsMulti-omicsMore Related Videos
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