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Visualizing metabolic network dynamics through time-series metabolomic data.

Lea F BuchweitzJames T YurkovichChristoph BlessingVeronika KohlerFabian SchwarzkopfZachary A KingLaurence YangFreyr JóhannssonÓlafur E SigurjónssonÓttar RolfssonJulian HeinrichAndreas Dräger
Published in: BMC bioinformatics (2020)
The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user's guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.
Keyphrases
  • electronic health record
  • big data
  • data analysis
  • machine learning
  • cross sectional
  • single cell
  • artificial intelligence