Data sciences of biological imaging: Image-based quantitative phenotyping
Biology is increasingly quantitative. In particular, ever-bigger datasets are acquired and sophisticated methods from the wider data sciences are now a part of the quantitative biology toolbox. In this EA we will see a wide range of data science techniques in the context of image based quantitative phenotyping with high content/high throughput microscopy.
Plan: 6 session of courses/practicals during which a published datasets from an academic paper will be reanalysed, allowing us to cover all step of such an analysis pipeline: image processing, image analysis, quantitative features computation, statistical analysis, machine learning and 'deep learning', and links with computational biology at large with ontologies and online databases. They will be followed by 3 more open project sessions to go further into one particular topic or another dataset. Evaluation will be an oral on the project.
Requirements: Formally none, to allow any interested student to potentially enrol. In practice, a real interest for biology in general, and a certain familiarity with the Python programming language will be needed.
Langue du cours : English
- Teaching coordinator: Chessel Anatole