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Benchmarking network algorithms for contextualizing genes of interest

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Listed:
  • Abby Hill
  • Scott Gleim
  • Florian Kiefer
  • Frederic Sigoillot
  • Joseph Loureiro
  • Jeremy Jenkins
  • Melody K Morris

Abstract

Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks.Author summary: In our labs, we aimed to use network algorithms to contextualize hits from functional genomics screens and gene expression studies. In order to understand how to apply these algorithms to our data, we characterized seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input.

Suggested Citation

  • Abby Hill & Scott Gleim & Florian Kiefer & Frederic Sigoillot & Joseph Loureiro & Jeremy Jenkins & Melody K Morris, 2019. "Benchmarking network algorithms for contextualizing genes of interest," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-14, December.
    • Handle: RePEc:plo:pcbi00:1007403
    DOI: 10.1371/journal.pcbi.1007403
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