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Comparing flux networks through weighted graphs alignment

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present the importance of flux network analysis as weighted graphs and one of the main tasks we perform on them as it is the comparison as a first approach. With the introduction of a new simple and low cost comparison based on previous developed algorithms to compare general graphs and metabolic pathways. We propose two alternative approaches to analyze the associated weighted graphs of flux networks and provide a fast but accurate scoring of its flux similarities on the first place and a list of similarities and differences between the given graphs as second, listed as pathways or individual edges. In this work we propose an extension as a simple way to compare weighted graphs (not considered before) to be applied as possible flux analysis. We provide insights about the simple analysis follow to get a good score system when comparing weighted graphs in a low cost computation. Also we provide and alternative way to get a valid comparison beyond a score, that is given to the interested user a more intuitive way to look for the similar data (or differences) during the analysis.

Original languageEnglish
Title of host publicationIWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-98
Number of pages6
ISBN (Electronic)9781728109671
DOIs
StatePublished - Jul 2019
Event2019 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2019 - Budapest, Hungary
Duration: 3 Jul 20195 Jul 2019

Publication series

NameIWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings

Conference

Conference2019 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2019
Country/TerritoryHungary
CityBudapest
Period3/07/195/07/19

Keywords

  • global alignment
  • graph traversal
  • local alignment
  • Needleman-Wunsch algorithm
  • Smith-Waterman algorithm

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