R01AG085581
Project Grant
Overview
Grant Description
Construction and application of comprehensive knowledge graphs for Alzheimer's disease - project summary/abstract
Despite significant advances in omics studies and laboratory observations, identifying the causal mechanisms for late-onset Alzheimer's disease (AD) and AD-related dementia (ADRD) remains a challenge due to their multifactorial inheritance, which is heterogeneous across subjects and populations.
In dementia studies, only a subset of relevant data is typically collected from each participant, resulting in a biased exploration of AD pathogenesis.
To address these limitations and respond to the NOT-AG-21-045 call for proposals on harmonizing complex data sets relevant to AD/ADRD, we propose the development of a comprehensive AD-related knowledge graph (AD-KG) platform.
This platform will integrate and harmonize data from multiple curated resources, literature, and a large-scale AD-related database with multiple types of high-dimensional data, including imaging, genetics, and clinical variables, from different studies with different missing-data patterns.
Our inspiration for this proposal comes from the success of knowledge graphs (KGs) as a foundation for cognitive systems in industry, such as Microsoft XiaOICE.
We propose to achieve four aims:
Aim 1: Construct a dynamic AD-KG by utilizing multiple curated resources, literature, and individual data across different domains and studies for AD.
Aim 2: Develop an omics knowledge graph (OKG) platform for harmonizing, imputing, and representing AD-related multi-omics data.
Aim 3: Develop a neuroimaging knowledge graph (NKG) platform for presenting, imputing, and representing AD-related neuroimaging data and a neuroimaging omics knowledge graph (NOKG) platform for neuroimaging-omics association maps.
Aim 4: Analyze data from the large-scale AD-related database and verify the effectiveness of the newly developed AD-KG for clinically transformative research.
The construction of OKG, NKG, and NOKG will complement the development of AD-KG.
By developing a comprehensive AD-KG platform that integrates diverse data sources and types, we can facilitate more comprehensive research in AD and ADRD, leading to the development of new diagnostic and therapeutic approaches.
Achieving these aims will offer unique analytic and data science capabilities necessary for AD-related cognitive systems and greatly enhance cognitive techniques through innovative use of semantics and graphs to address complex data modeling, blending, and analytic challenges.
To achieve our proposal aims, we have formed a team of experts in cognitive systems, knowledge graphs, statistical genetics/genomics, AD genetics, neuroimaging analysis, neuroscience, and statistics.
Clinically, achieving these aims will enhance the identification of new genes and genetic pathways, leading to risk and protective factors for AD and inspiring novel therapeutic approaches.
We plan to share our AD-KG, new cognitive tools, and AD-KG-based structured data with the research community through NIAGADS and other NIA infrastructure.
Despite significant advances in omics studies and laboratory observations, identifying the causal mechanisms for late-onset Alzheimer's disease (AD) and AD-related dementia (ADRD) remains a challenge due to their multifactorial inheritance, which is heterogeneous across subjects and populations.
In dementia studies, only a subset of relevant data is typically collected from each participant, resulting in a biased exploration of AD pathogenesis.
To address these limitations and respond to the NOT-AG-21-045 call for proposals on harmonizing complex data sets relevant to AD/ADRD, we propose the development of a comprehensive AD-related knowledge graph (AD-KG) platform.
This platform will integrate and harmonize data from multiple curated resources, literature, and a large-scale AD-related database with multiple types of high-dimensional data, including imaging, genetics, and clinical variables, from different studies with different missing-data patterns.
Our inspiration for this proposal comes from the success of knowledge graphs (KGs) as a foundation for cognitive systems in industry, such as Microsoft XiaOICE.
We propose to achieve four aims:
Aim 1: Construct a dynamic AD-KG by utilizing multiple curated resources, literature, and individual data across different domains and studies for AD.
Aim 2: Develop an omics knowledge graph (OKG) platform for harmonizing, imputing, and representing AD-related multi-omics data.
Aim 3: Develop a neuroimaging knowledge graph (NKG) platform for presenting, imputing, and representing AD-related neuroimaging data and a neuroimaging omics knowledge graph (NOKG) platform for neuroimaging-omics association maps.
Aim 4: Analyze data from the large-scale AD-related database and verify the effectiveness of the newly developed AD-KG for clinically transformative research.
The construction of OKG, NKG, and NOKG will complement the development of AD-KG.
By developing a comprehensive AD-KG platform that integrates diverse data sources and types, we can facilitate more comprehensive research in AD and ADRD, leading to the development of new diagnostic and therapeutic approaches.
Achieving these aims will offer unique analytic and data science capabilities necessary for AD-related cognitive systems and greatly enhance cognitive techniques through innovative use of semantics and graphs to address complex data modeling, blending, and analytic challenges.
To achieve our proposal aims, we have formed a team of experts in cognitive systems, knowledge graphs, statistical genetics/genomics, AD genetics, neuroimaging analysis, neuroscience, and statistics.
Clinically, achieving these aims will enhance the identification of new genes and genetic pathways, leading to risk and protective factors for AD and inspiring novel therapeutic approaches.
We plan to share our AD-KG, new cognitive tools, and AD-KG-based structured data with the research community through NIAGADS and other NIA infrastructure.
Funding Goals
NOT APPLICABLE
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Chapel Hill,
North Carolina
27599
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 190% from $1,283,911 to $3,726,887.
University Of North Carolina At Chapel Hill was awarded
Comprehensive Alzheimer's Disease Knowledge Graphs Enhanced Research
Project Grant R01AG085581
worth $3,726,887
from National Institute on Aging in September 2024 with work to be completed primarily in Chapel Hill North Carolina United States.
The grant
has a duration of 4 years 9 months and
was awarded through assistance program 93.866 Aging Research.
The Project Grant was awarded through grant opportunity Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R01 Clinical Trial Optional).
Status
(Ongoing)
Last Modified 7/6/26
Period of Performance
9/30/24
Start Date
6/30/29
End Date
Funding Split
$3.7M
Federal Obligation
$0.0
Non-Federal Obligation
$3.7M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01AG085581
Transaction History
Modifications to R01AG085581
Additional Detail
Award ID FAIN
R01AG085581
SAI Number
R01AG085581-2501713186
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
75NN00 NIH National Insitute on Aging
Funding Office
75NN00 NIH National Insitute on Aging
Awardee UEI
D3LHU66KBLD5
Awardee CAGE
4B856
Performance District
NC-04
Senators
Thom Tillis
Ted Budd
Ted Budd
Modified: 7/6/26