Graph learning for genomic repeat detection
Graph neural networks and self-supervised learning, applied to a framework that uses graph embeddings to detect repeats in genomes.
- When
- Summer 2026
- Role
- Undergraduate Researcher
- Lab
- Segarra Lab, Graph Signal Processing (GSP) & Machine Learning Laboratory
- Institution
- Rice University
- Topics
- Graph neural networks
- Self-supervised learning
- Genomics
- Methods
- GCN
- GraphSAGE
- GAT
- Graph embeddings
Researched graph neural networks (GCN, GraphSAGE, GAT) and self-supervised learning, developing a framework using graph embeddings and machine learning for genomic repeat detection.