Description
The objective of this project is to develop a visual analytics tool that combines radiogenomic data and pathway analysis to gain insights into the relationship between genetic profiles, radiological features, and molecular pathways in cancer.
The project focuses on creating an interactive and visually intuitive platform that allows researchers and clinicians to explore and analyze radiogenomic data in the context of molecular pathways. The tool integrates radiological imaging data, genomic profiles, and pathway information to provide a comprehensive understanding of the underlying biological processes associated with specific radiological features observed in cancer patients.
Some (general) references:
1. https://www.frontiersin.org/articles/10.3389/fphys.2015.00383/full
2. https://arxiv.org/abs/2110.04808
3. https://www.frontiersin.org/articles/10.3389/fonc.2020.570465/full
4. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14172
Tasks
- Pathway Analysis
- Integration of Pathways with Radiogenomic Data
- Interactive Filtering and Exploration
- Predictive Analytics and Machine Learning
Requirements
- Interest and knowledge in biomedical visualization and visual analytics (you should have taken at least one visualization course from our group).
- Knowledge and experience with (unsupervised) machine learning.
- Good programming skills (python, javascript).
- Creativity and enthusiasm.
Environment
The project builds upon an existing web-based implementation that combines python (backend for the radiomics analytics) and javascript (d3.js).