Centroids
Explainability framework for dimensionality reduction plots in neurogenetic datasets.
Centroids improves interpretability for dimensionality-reduction outputs used in biomedical machine learning.
Project scope:
- Implemented PCA, t-SNE, and UMAP pipelines for high-dimensional neurogenetic data analysis.
- Added feature-importance-driven ranking and centroid overlays for more interpretable embedding views.
- Automated large parameter sweeps for reduction methods to accelerate reproducible experimentation.
This work contributed to our IEEE CIBCB 2024 publication, which received the Best Paper Award.