Developing, Applying, and Explaining Graph Convolutional Neural Networks in the Context of Genome Wide Association Analyses to Increase Attention Deficit/Hyperactivity Disorder Prediction Accuracy, Extract Novel Genomic Networks Associated With Attention

在全基因组关联分析的背景下开发、应用和解释图卷积神经网络，以提高注意力缺陷/多动症预测准确性，提取与注意力相关的新型基因组网络

• 批准号: 457223
• 负责人: Saxena Ankita
• 金额: $ 7.65万
• 依托单位: SUNY Upstate Medical University (Syracuse, New York)
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-10-01 至 2024-10-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=457223&lang=en
• 关键词: Adhd; Behavioral Disorders; Deep Learning; Genetics; Genomics; Machine Learning; Neural Network; Psychiatric Diseases

Attention Deficit/Hyperactivity Disorder (ADHD) is a common psychiatric disorder that impairs patients' quality of life. Many factors, including genetics contribute to the disorder. Large scale studies of the genetics of people with and without ADHD have

注意力缺陷多动障碍（ADHD）是一种常见的精神疾病，损害患者的生活质量。许多因素，包括遗传学，都导致了这种疾病。对患有和不患有ADHD的人的遗传学进行的大规模研究