Computational methods can help to unravel the complexity of the tumor microenvironment of individual patients. We ask for your help to assess such methods to enhance our understanding of cancer heterogeneity and to improve patient classification.
Apply until Nov 30, 2020 / Xplorers Camp on Dec 11, 2020
Can patients be stratified by their immunological tumor environment and does this additional information improve classification models?
It has been shown that the levels of tumor infiltrating immune cells are associated with tumor growth, disease progression and patient outcome. Common methods for studying cell heterogeneity such as flow cytometry are limited by cell type marker selection and sample processing steps. Recent computational methods enable the prediction of cell type frequencies in tumor samples solely from gene expression data. An ideal method for feature engineering in the context of patient stratification.
Team Lead Data Science
Preferred scale: 3 months full-time (flexible models are also possible)
Possible format: Internship
Show us how you would approach the problem. You can prepare your idea proposal in 3-5 slides, any other idea/format to share your proposal with us is also welcome. We do not expect a bullet-proof solution to the problem.
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