# Computational Techniques and Resources for Effective Translational Research in Alzheimer's Disease

> **NIH NIH R13** · JACKSON LABORATORY · 2024 · $50,000

## Abstract

PROJECT SUMMARY / ABSTRACT
Alzheimer’s Disease (AD) is a complex and heterogenous disorder characterized by multiple clinical,
neuropathological and molecular phenotypes. Despite considerable effort to date, there is currently a lack of
interventions or cure, thus there is a clear need for a better understanding of the heterogenous disease
phenotypes and the underlying genetics to identify more effective preclinical strategies. Rapidly expanding
genomic, transcriptomic, proteomic, metabolomic and epigenomic data sets, and emerging animal models of AD
created by the Model Organism Development and Evaluation for Late-Onset Alzheimer’s Disease (MODEL-AD)
consortium, now provide new tools for accelerating our understanding of AD and enable interspecies analysis
for mapping findings from mouse model systems of AD to human omics signatures. While multi-omics data sets
derived from AD and animal models of AD are available to the scientific community through various data
ecosystems including the AD Knowledge Portal, a NIA designated FAIR (Findable, Accessible, Interoperable,
and Reusable) data repository, a current barrier is mapping disease relevant molecular signatures between
model organisms and humans. Thus, there is an emerging need to provide training in bioinformatics
methodologies for systematic interspecies translation of omics-derived signatures of AD. To address this need,
we aim to provide a new, and increasingly diverse, generation of researchers, with awareness of available
resources and to provide skills development for integrating multi-scale data from model systems and humans to
advance AD research and interventions. We propose a unique, annual, 4-day workshop at The Jackson
Laboratory (JAX), that will leverage trainers and expertise from Sage Bionetwork, the host institution for The AD
Knowledge Portal and Exceptional Longevity Data Management and Coordinating Center, and the MODEL-AD
Center at JAX. The proposed workshop, Computational Techniques and Resources for Effective Translational
Research in Alzheimer’s Disease, will focus on enabling utilization of omics-driven computational techniques
and analytical principles for cross species functional alignment. Active learning programming sessions will be
the core of the workshop, in combination with lectures and interactive forums. Participants will emerge from the
workshop equipped with the knowledge and technical skills to conduct rigorous and reproducible computational
research for more effective translational strategies in AD. To achieve this, we propose the following aims: 1)
foster utilization of existing data resources from human and model organism studies in AD; 2) deliver hands-on
training on computational techniques that reinforce rigor and reproducibility principles for translational AD
research, and; 3) create an inclusive environment for trainees that fosters networking, collaboration, and
experiential learning. Pro-active strategies, including provision of scholarships, wil...

## Key facts

- **NIH application ID:** 11000451
- **Project number:** 1R13AG089980-01
- **Recipient organization:** JACKSON LABORATORY
- **Principal Investigator:** Asli Uyar
- **Activity code:** R13 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $50,000
- **Award type:** 1
- **Project period:** 2024-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11000451

## Citation

> US National Institutes of Health, RePORTER application 11000451, Computational Techniques and Resources for Effective Translational Research in Alzheimer's Disease (1R13AG089980-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11000451. Licensed CC0.

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