Improving the accuracy and speed of biological sequence alignment using machine learning
Machine learning approaches for improved accuracy and speed in sequence annotation
This study is working on improving how scientists compare biological sequences to better understand their evolution and function, using smart computer techniques to make the process faster and more accurate, which will help researchers in their work.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Arizona NIH-funded |
| Lab location | 1 site (Tucson, United States) |
| Project ID | NIH-10838066 on NIH RePORTER |
What this research studies
This research focuses on enhancing the methods used to align biological sequences, which is crucial for understanding their evolution and function. By employing machine learning techniques, the project aims to reduce errors in sequence annotation caused by complex and repetitive sequences. Additionally, it seeks to speed up the alignment process through a custom deep learning architecture that filters candidate sequences efficiently. The outcomes will be integrated into popular bioinformatics tools, benefiting researchers in the field.
Who could benefit from this research
Good fit: Ideal candidates for participation or benefit from this research include researchers and scientists working in genetics, bioinformatics, and related fields who rely on sequence alignment tools.
Not a fit: Patients who are not involved in research or do not work in fields related to genetics or bioinformatics may not receive direct benefits from this research.
Why it matters
Potential benefit: If successful, this research could significantly improve the accuracy and efficiency of biological sequence analysis, leading to better insights in genetics and evolutionary biology.
How similar studies have performed: Other research has shown success with machine learning approaches in bioinformatics, indicating a promising avenue for improving sequence alignment methods.
Where this research is happening
Tucson, United States
- University of Arizona — Tucson, United States (Active)
Researchers
- Principal investigator: Wheeler, Travis John — University of Arizona
- Study coordinator: Wheeler, Travis John
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.