# Framework for a series of high priority multiscale analyses

> **NIH NIH U24** · SAGE BIONETWORKS · 2020 · $156,188

## Abstract

Project Summary/Abstract
Alzheimer’s Disease (AD) is a debilitating neurodegenerative disease affecting more than 5 million Americans.
Despite significant investment in drug discovery and development, no therapeutic options yet exist that can
prevent, slow, or cure AD. The Accelerating Medicines Partnership in Alzheimer’s Disease Target Discovery and
Preclinical Validation project (AMP-AD) was designed to help address this problem by identifying candidate
targets through evaluation of AD-induced changes in human molecular state on a systems level. The program
uses an open science paradigm to support early, iterative integration of resources and evaluation of findings
across multiple independent teams. To extend this work we propose a cross team analytic effort to 1. Create a
machine learning model of temporal Alzheimer’s disease progression. 2. Harmonize CNS model of disease
progression and peripheral measures of disease state, and 3. Combine heterogeneous biomolecular networks
with the molecular model of disease progression for a unified multi-scale model of disease mechanism and
progression. This will amplify the impact of the individual team’s efforts, and help disentangle the molecular and
temporal complexity of this devastating disease.

## Key facts

- **NIH application ID:** 10071815
- **Project number:** 3U24AG061340-02S1
- **Recipient organization:** SAGE BIONETWORKS
- **Principal Investigator:** LARA M MANGRAVITE
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $156,188
- **Award type:** 3
- **Project period:** 2018-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10071815

## Citation

> US National Institutes of Health, RePORTER application 10071815, Framework for a series of high priority multiscale analyses (3U24AG061340-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10071815. Licensed CC0.

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