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SUMMARY:Bayesian networks for probing complex biological and biology-adjac
 ent systems - Dr V Anne Smith\, School of Biology\, University of St Andre
 ws
DTSTART:20240625T130000Z
DTEND:20240625T140000Z
UID:TALK218287@talks.cam.ac.uk
CONTACT:Michael Boemo
DESCRIPTION:I present work in my group using Bayesian network models for e
 xploring interactions within complex biological systems (e.g.\, genetic re
 gulatory networks\, neuronal networks\, ecosystems) and biology-adjacent s
 ystems (e.g.\, sociobiological/health systems). These systems consist of d
 ense webs of interactions among many elements. For example\, genes can reg
 ulate the expression level of other genes as well as react to the environm
 ent\; neurons activate or inhibit other neurons plus can be modulated by s
 ensory input\; organisms predate upon and compete with each other while re
 sponding to abiotic factors\; patient behaviour is influenced by a host of
  socioeconomic factors and past experiences. However\, often these interac
 tions are unknown and must be learnt from observational data. Here\, I pre
 sent our work in developing algorithms for this task\, known as the networ
 k inference problem\; particularly\, I cover advances we’ve made to Baye
 sian networks and their machine learning approaches for characteristics of
  specific systems\, and tools we’ve developed for helping to apply these
  models. I provide examples of how we use inferred networks to enhance kno
 wledge discovery across these many types of complex systems.
LOCATION:Part II Room\, Department of Genetics
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