AD ADRD Databases: Organizing Key Research Domains to Enhance Diagnostics and Treatment
Disciplines
Diagnosis | Genetic Phenomena | Medical Biochemistry | Medical Biotechnology | Telemedicine | Therapeutics
Abstract (300 words maximum)
Alzheimer’s Disease (AD) is a neurodegenerative disorder that progressively characterized by cognitive decline, memory loss, and behavioral changes. AD belongs Alzheimer’s Disease-Related Dementias which is a broader term used to classify neurodegenerative disorders. According to a study by the National Institute of Aging, AD is currently the 7th leading cause of death in the United States. As research for this disease advances, new computational methods have shown promise in improving diagnosis, understanding of disease mechanisms, and identifying potential treatments. However, today researchers are finding it hard to navigate the vast landscape of AD ADRD databases which are dispersed across domains and difficult to integrate. To help with this, this works conducts a structure review and comparative analysis of databases that are publicly availble and evaluate on different criteria such as accessibility, data type, metadata quality, and integration of datasets with external applications. Our results was that we were able to classify 8 databases into 3 primary categories: clinical and population data, genetics and genomics, and drug discovery and therapeutics.
Use of AI Disclaimer
no
Academic department under which the project should be listed
CCSE – Information Technology
Primary Investigator (PI) Name
Chloe Yixin Xie
AD ADRD Databases: Organizing Key Research Domains to Enhance Diagnostics and Treatment
Alzheimer’s Disease (AD) is a neurodegenerative disorder that progressively characterized by cognitive decline, memory loss, and behavioral changes. AD belongs Alzheimer’s Disease-Related Dementias which is a broader term used to classify neurodegenerative disorders. According to a study by the National Institute of Aging, AD is currently the 7th leading cause of death in the United States. As research for this disease advances, new computational methods have shown promise in improving diagnosis, understanding of disease mechanisms, and identifying potential treatments. However, today researchers are finding it hard to navigate the vast landscape of AD ADRD databases which are dispersed across domains and difficult to integrate. To help with this, this works conducts a structure review and comparative analysis of databases that are publicly availble and evaluate on different criteria such as accessibility, data type, metadata quality, and integration of datasets with external applications. Our results was that we were able to classify 8 databases into 3 primary categories: clinical and population data, genetics and genomics, and drug discovery and therapeutics.