​​Seminar on Graph-based Causal Discovery in Computational Biology

🎓 Topic: "Causal discovery from multivariate information in biological and biomedical data"
👨‍🔬 Who: Hervé Isambert, The Isambert Lab, CNRS, Institut Curie, Paris
⌚ When: Monday, July 29th, 5pm CEST

Abstract: In this webinar, I will present the principles and limitations of graph-based causal discovery methods and their improvement using multivariate information decomposition, recently developed in my lab. Applications will range from gene expression data in single cells to nationwide medical databases of cancer patients. I will then discuss the theoretical link between graph-based causality and temporal (Granger-Schreiber) causality, which can both be expressed in terms of conditional multivariate information. While temporal causality is shown to imply graph-based causality, the converse may not be true (see Figure). An application to time series data concerns the analysis of video images of reconstituted tumor ecosystems, which uncovered a novel antagonistic effect of cell-cell interactions under therapeutically relevant conditions.

The Zoom link will appear in this channel shortly before 5pm