Understanding ALS through advanced data analysis
RFA-TS-22-001:Towards Better Understanding of ALS using a Multi-Marker Discovery Approach from a Multi-Modal Database (ALS4M)
This study is looking to learn more about ALS by using advanced technology and lots of health data to find new risk factors and improve how we diagnose the disease, so we can better understand it and help those affected.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Missouri-Columbia NIH-funded |
| Lab location | 1 site (Columbia, United States) |
| Project ID | NIH-10873667 on NIH RePORTER |
What this research studies
This research aims to enhance our understanding of Amyotrophic Lateral Sclerosis (ALS) by utilizing large, multi-modal data resources and machine-learning algorithms. The study focuses on identifying risk factors and improving diagnostic methods for ALS, a rare and fatal neurodegenerative disorder. By analyzing diverse healthcare records and linking them to other relevant data, the research seeks to uncover novel associations and causalities that have not been explored in traditional studies. This innovative approach aims to create a more statistically powerful study population to better understand the complexities of ALS.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals diagnosed with ALS or those at risk of developing the condition.
Not a fit: Patients with ALS who have well-defined genetic causes may not benefit from this research as it focuses on sporadic cases and unidentified risk factors.
Why it matters
Potential benefit: If successful, this research could lead to improved diagnosis and a better understanding of risk factors for ALS, potentially enhancing patient care and treatment options.
How similar studies have performed: While there have been studies on ALS, this approach using machine learning and multi-modal data is relatively novel and has not been extensively tested.
Where this research is happening
Columbia, United States
- University of Missouri-Columbia — Columbia, United States (Active)
Researchers
- Principal investigator: Song, Xing — University of Missouri-Columbia
- Study coordinator: Song, Xing
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 below.