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SUMMARY:Causal Discovery and Network Inference for Biological and Biomedic
 al Data - Prof. Hervé Isambert\, Institut Curie
DTSTART:20251106T150000Z
DTEND:20251106T160000Z
UID:TALK239659@talks.cam.ac.uk
CONTACT:Sarah Morgan
DESCRIPTION:Discovering causal effects is at the core of scientific invest
 igation but remains challenging when mostly observational data is availabl
 e. In essence\, causal discovery infers cause-effect relations from specif
 ic correlation patterns involving at least three variables\, which goes be
 yond the popular notion that pairwise correlation does not imply causation
 . Yet\, in practice\, causal representations have been difficult to learn 
 and interpret\, in particular\, for high dimensional data such as state-of
 -the-art biological and biomedical data.\n\nIn this talk\, I will outline 
 some network reconstruction methods and a broad range of applications. In 
 particular\, our group has developed novel causal discovery methods and to
 ols (i.e. MIIC\, CausalXtract\, MIIC-sdg\, CausalCCC\, MIIC seach&score) t
 o learn cause-effect relationships in a variety of biological or biomedica
 l data\, from single-cell transcriptomics and live-cell imaging data to cl
 inical data from medical records of patients. These Machine Learning metho
 ds combine multivariate information analysis with interpretable graphical 
 models and outperform other methods on a broad range of benchmarks\, in pa
 rticular on complex non-linear datasets\, while allowing for unobserved la
 tent variables\, that are ubiquitous in biomedical applications. 
LOCATION:Online
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