Functional association networks as priors for gene regulatory network inference

Matthew E. Studham, Andreas Tjärnberg, Torbjörn E.M. Nordling, Sven Nelander, Erik L.L. Sonnhammer

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data.

Original languageEnglish
Pages (from-to)I130-I138
JournalBioinformatics
Volume30
Issue number12
DOIs
Publication statusPublished - 2014 Jun 15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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