Identifying interactive biological pathways associated with reading disability-Reference-Cited by-同舟云学术

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Identifying interactive biological pathways associated with reading disability

Published:2020-06-28 Issue:8 Volume:10 Page:
ISSN:2162-3279
Container-title:Brain and Behavior
language:en
Short-container-title:Brain Behav

Author:

Lancaster Hope Sparks¹²^ORCID,Liu Xiaonan²,Dinu Valentin¹,Li Jing³

Affiliation:

1. College of Health Solutions Arizona State University Tempe AZ USA

2. Department of Computing, Informatics, and Decision Systems Engineering Schools of Engineering Arizona State University Tempe AZ USA

3. School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA USA

Funder

National Institute of Child Health and Human Development

Publisher

Wiley

Subject

Behavioral Neuroscience

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/brb3.1735

Reference94 articles.

1. Genetic and environmental risk factors for developmental dyslexia in children: systematic review of the last decade

2. Learning Disorders With a Special Emphasis on Reading Disorders: A Review of the Past 10 Years

3. Dyslexic Children Show Short-Term Memory Deficits in Phonological Storage and Serial Rehearsal: An fMRI Study

4. Paths to Reading Comprehension in At-Risk Second-Grade Readers

5. Cohort Profile: The ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children

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1. Joint exome and metabolome analysis in individuals with dyslexia: Evidence for associated dysregulations of olfactory perception and autoimmune functions;2024-07-02

2. Genome-Wide Association Study of Gallstone Disease Identifies Novel Candidate Genomic Variants in a Latino Community of Southwest USA;Journal of Racial and Ethnic Health Disparities;2023-11-28

3. A data-fusion approach to identifying developmental dyslexia from multi-omics datasets;2023-02-28

4. Exploring Genetic and Neural Risk of Specific Reading Disability within a Nuclear Twin Family Case Study: A Translational Clinical Application;Journal of Personalized Medicine;2023-01-14

5. Machine and deep learning identified metabolites and clinical features associated with gallstone disease;Computer Methods and Programs in Biomedicine Update;2023

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