Improving data analysis for Alzheimer's disease using advanced computational methods
Official title: Imputing quantitative mass spectrometry proteomics data using non-negative matrix factorization
Funded at University of Washington · NIH-11092699 · Data updated 2026-11-12
This study is working on new computer tools to help scientists better understand Alzheimer's disease by filling in gaps in important data from experiments, making it easier for researchers to analyze and learn more about the condition.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | April 16, 2023 |
| Project end | April 15, 2026 |
| Funding year | 2025 |
| Award (this year) | $39,491 |
| NIH project number | 5F31AG082395-03 |
| Project ID | NIH-11092699 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with Alzheimer's disease or those at risk of developing it, as they may benefit from improved diagnostic and treatment strategies.
✕Less likely to help
Patients with non-Alzheimer's forms of dementia or those without any cognitive impairment may not receive direct benefits from this research.
What this research studies
This research focuses on enhancing the analysis of mass spectrometry data related to Alzheimer's disease by developing advanced computational techniques. It aims to address the issue of missing data in proteomics experiments, which can hinder the understanding of the disease. By employing machine learning methods, specifically non-negative matrix factorization and deep neural networks, the project seeks to estimate missing values in the data. The resulting software tools will be user-friendly and open-source, making them accessible for researchers in the field.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and reproducible findings in Alzheimer's disease research, potentially accelerating the discovery of new biomarkers and treatment targets.
How similar studies have gone: Other research has shown success in using machine learning approaches for data imputation in various fields, suggesting a promising potential for this novel application in Alzheimer's research.
Where this research is happening
-
University of Washington
Seattle, United States Active
Researchers
- Principal investigator: Harris, Lincoln Jeffery — University of Washington
- Study coordinator: Harris, Lincoln Jeffery
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.