🚀 @SBERLOGABIO webinar on bionformatics and data science:
👨🔬 Hervé Isambert, CNRS, Institut Curie, Paris "Causal discovery from multivariate information in biological and biomedical data"
⌚️ Monday 29 July at 18.00 (Moscow time)
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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.
http://kinefold.curie.fr/isambertlab
Zoom link will be in @sberlogabig just before start. Video records: https://www.youtube.com/c/SciBerloga - subscribe !
📹 Video: https://youtu.be/22YKS6lKyfg?si=U5iDJC0JXIJYpg_-
📖 Presentation: https://t.me/sberlogabio/77724
👨🔬 Hervé Isambert, CNRS, Institut Curie, Paris "Causal discovery from multivariate information in biological and biomedical data"
⌚️ Monday 29 July at 18.00 (Moscow time)
Add to Google Calendar
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.
http://kinefold.curie.fr/isambertlab
Zoom link will be in @sberlogabig just before start. Video records: https://www.youtube.com/c/SciBerloga - subscribe !
📹 Video: https://youtu.be/22YKS6lKyfg?si=U5iDJC0JXIJYpg_-
📖 Presentation: https://t.me/sberlogabio/77724