e-learning
Modeling Breast Cancer Subtypes with Flexynesis
Abstract
Flexynesis represents a state-of-the-art deep learning framework specifically designed for multi-modal data integration in biological research. What sets Flexynesis apart is its comprehensive suite of deep learning architectures that can handle various data integration scenarios while providing robust feature selection and hyperparameter optimization.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How can we model breast cancer subtypes using transcriptomics and genomic data?
- What are the key expression patterns that distinguish different BRCA subtypes?
- How can we interpret the learned features from a deep neural network classifier?
Learning Objectives
- Apply Flexynesis to model and visualize BRCA subtypes
- Interpret the learned representations from the DirectPred model
- Use UMAP and clustering to explore learned features
Licence: Creative Commons Attribution 4.0 International
Keywords: Statistics and machine learning
Target audience: Students
Resource type: e-learning
Version: 2
Status: Active
Learning objectives:
- Apply Flexynesis to model and visualize BRCA subtypes
- Interpret the learned representations from the DirectPred model
- Use UMAP and clustering to explore learned features
Date modified: 2025-08-13
Date published: 2025-08-13
Scientific topics: Statistics and probability
Activity log
