Global Similarity Between Multiple Bionetworks

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Global Similarity Between Multiple Bionetworks

• Yunkai Liu
• Computer Science Department
• University of South Dakota

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Background

• Just as the rapid disclosing of genomic data
  enables the study of sequence conservation, the
  growth of network quality and availability allows
  us to ask similar questions at network level.
• One challenging problem is the characterization
  of similar patterns among multiple biological
  networks. However, there is no definition of
  similarity between networks that has been agreed
  upon and efficient algorithms for comparing
  dynamic bio-networks are limited.

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Graph Model and Previous Works

• The growth of quality and availability of new
  biotechnology allows us to simulate biological
  systems with graph models. Generally speaking,
  nodes represent biological units (e.g., proteins
  or genes) and edges represent physical or
  chemical relationships.
• Previous works PHUNKEE (2007) Græmlin (2006)
  NetworkBLAST (2004)

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Purpose and Significance

• The global similarity of multiple bio-networks,
  such as anatomical networks, gene regulatory
  network and protein interaction networks, are
  expected to evaluate the overall topological
  likeness among graphs.
• Biological Applications
• Topological structural study
• Evolution of bio networks
• Experimental data analysis

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Method

• Basic method compare the adjacent matrices of
  networks.
• Challenges
• Sequence sorting The nodes are generally
  weighted by different attributes however, the
  occurrence of same nodes in graphs greatly
  increase the complexity for finding the maximal
  global similarities between two networks.
• Transitivity Especially in functional networks,
  the transitivity should be considered. Another
  reason is to allow gaps in study.
• Global and Local similarity The optimal solution
  of global similarity may cause the ignorance of
  local conserved subgraphs. The comparison of
  similar graphs may have noises.

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Current and Future Work

• Currently, a research team, consisting of
  researchers from Computer Science Dept and
  Medical School, are developing software and
  biological applications based on bionetwork
  comparison.
• We are also looking for collaborators.
• Please contact, Yunkai.Liu_at_usd.edu