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SCH: Novel Multi-View Statistical Machine Learning for Alzheimer's Disease

Award Number: RF1AG098697
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/05/2025
PERIOD OF PERFORMANCE END DATE: 08/31/2029

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SCH: Novel Multi-View Statistical Machine Learning for Alzheimer's Disease - Alzheimer’s disease (AD) is the most common type of dementia without curative medications. Early detection of AD is thus essential for timely intervention and effective treatment development. Multi-view data provides a transformative approach to enhance the understanding, diagnosis, and prediction of AD. This research aims to develop three novel statistical machine learning methods for AD research using multi-view imaging, genomic, and clinical data, focusing on multi-view brain and genomic network analysis, significant differential feature testing, and diagnosis and survival analysis. The specific aims of this proposal include: 1. Develop a novel high-dimensional multi-view data decomposition based on uncorrelated common and distinctive latent factors (C&DLFs) to construct multi-view networks, with application to comparing brain and genomic networks across AD statuses; 2. Develop an optimal false discovery rate (FDR) control method based on a novel semi-parametric hidden Markov random field for high-dimensional spatial multiple testing, with application to identifying significant brain and genomic differences across AD statuses; 3. Develop a highly accurate deep-learning-based diagnosis and survival framework for high-dimensional tabular data, incorporating feature selection and view ablation to enhance cost-effectiveness and data accessibility in clinical practice; 4. Apply the three proposed methods to four large-scale AD-related datasets and disseminate the methods with an open, efficient software package. The three novel methods in our project will undergo rigorous theoretical and numerical analyses. The research team’s extensive expertise in multi-view data analysis, network analysis, imaging genetics, high- dimensional statistics, deep learning, and AD research will make significant contributions to the project’s success. RELEVANCE (See instructions): The research goal is to develop three novel statistical machine learning methods for Alzheimer’s disease (AD) using multi-view imaging, genomic, and clinical data. These methods will focus on multi-view brain and genomic network analysis, significant differential feature testing, and diagnosis and survival analysis. The proposed project will enhance our understanding of neuro-genetic associations in AD and significantly contribute to biomarker discovery, early detection, and improved patient survival in AD.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $1,012,879 )
2025	2025	NEW YORK UNIVERSITY	70 WASHINGTON SQ S	NEW YORK	NY	10012	NEW YORK	USA	Aging Research	000	1	9/4/2025	NEW	$1,012,879
														Subtotal = $1,012,879

Grand Total All Awards = $1,012,879

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SCH: Novel Multi-View Statistical Machine Learning for Alzheimer's Disease

Award Number: RF1AG098697

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/05/2025

PERIOD OF PERFORMANCE END DATE: 08/31/2029

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