Using deep learning to understand genetic variation and evolution
Advancing evolutionary genetics through deep learning
This study is looking at how advanced computer techniques can help us understand the genetic differences that affect how species evolve and adapt to their environments, which could lead to new insights about the diversity of life on Earth.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project ID | NIH-11089849 on NIH RePORTER |
What this research studies
This research investigates how deep learning techniques can be applied to analyze complex genomic data, particularly focusing on understanding the genetic variations that influence evolutionary processes. By utilizing multidimensional representations of genomic data, the project aims to improve the accuracy of evolutionary inferences, which can shed light on how species adapt to changing environments. The researchers will explore the use of advanced computational methods to analyze large datasets, potentially revealing insights into the genetic basis of adaptation and diversity.
Who could benefit from this research
Good fit: Ideal candidates for benefiting from this research include individuals interested in genetic diversity, evolutionary biology, and those affected by conditions influenced by genetic variation.
Not a fit: Patients with conditions unrelated to genetic variation or evolutionary processes may not receive direct benefits from this research.
Why it matters
Potential benefit: If successful, this research could enhance our understanding of genetic adaptation, leading to improved strategies for managing biodiversity and addressing challenges in conservation and agriculture.
How similar studies have performed: Other research has shown promise in using deep learning for genomic analysis, indicating that this approach could lead to significant advancements in the field.
Where this research is happening
Chapel Hill, United States
- Univ of North Carolina Chapel Hill — Chapel Hill, United States (Active)
Researchers
- Principal investigator: Schrider, Daniel R — Univ of North Carolina Chapel Hill
- Study coordinator: Schrider, Daniel R
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.