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UID:pretalx-juliacon-2026-ADYCYS@pretalx.com
DTSTART;TZID=CET:20260813T120000
DTEND;TZID=CET:20260813T121500
DESCRIPTION:Biology is driven by interactions: between transcription factor
 s and genes\, receptors and ligands\, and pre- and post-synaptic neurons. 
 To gain insights about complex systems\, embedding methods are commonly us
 ed to represent pairwise similarity relationships\; however\, we lack tool
 s for *coembedding* two or more classes of interacting objects. I will pre
 sent new machine learning tools for coembedding interacting systems. A dis
 tinguishing feature is the representation of entities by Gaussian probabil
 ity distributions rather than points\, which results in a quadratic compre
 ssion of dimensionality\, enabling quantiatively-accurate visualization of
  more complex systems than is possible by traditional techniques.
DTSTAMP:20260710T092531Z
LOCATION:Muschel — N1
SUMMARY:Geometric coembedding of complex interacting systems - Tim Holy
URL:https://pretalx.com/juliacon-2026/talk/ADYCYS/
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