Discovering reliable protein interactions from high-throughput experimental data using network topology

Jin Chen, Wynne Hsu, Mong Li Lee, See Kiong Ng

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Objective: Current protein-protein interaction (PPI) detection via high-throughput experimental methods, such as yeast-two-hybrid has been reported to be highly erroneous, leading to potentially costly spurious discoveries. This work introduces a novel measure called IRAP, i.e. "interaction reliability by alternative path", for assessing the reliability of protein interactions based on the underlying topology of the PPI network. Methods and materials: A candidate PPI is considered to be reliable if it is involved in a closed loop in which the alternative path of interactions between the two interacting proteins is strong. We devise an algorithm called AlternativePathFinder to compute the IRAP value for each interaction in a complex PPI network. Validation of the IRAP as a measure for assessing the reliability of PPIs is performed with extensive experiments on yeast PPI data. All the data used in our experiments can be downloaded from our supplementary data web site at∼chenjin/data.html. Results: Results show consistently that IRAP measure is an effective way for discovering reliable PPIs in large datasets of error-prone experimentally-derived PPIs. Results also indicate that IRAP is better than IG2, and markedly better than the more simplistic IG1 measure. Conclusion: Experimental results demonstrate that a global, system-wide approach - such as IRAP that considers the entire interaction network instead of merely local neighbors - is a much more promising approach for assessing the reliability of PPIs.

Original languageEnglish
Pages (from-to)37-47
Number of pages11
JournalArtificial Intelligence in Medicine
Issue number1-2
StatePublished - Sep 2005


  • Alternative path
  • Network topology
  • Protein interaction reliability

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence


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