Pathway-dependent effectiveness of network algorithms for gene prioritization

Jung Eun Shim, Sohyun Hwang, Insuk Lee

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms - direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network - in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity.

Original languageEnglish
Article numbere0130589
JournalPloS one
Issue number6
Publication statusPublished - 2015 Jun 19

Bibliographical note

Publisher Copyright:
© 2015 Shim et al.

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General


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