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

研究成果: Article同行評審

31 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)I130-I138
期刊Bioinformatics
30
發行號12
DOIs
出版狀態Published - 2014 6月 15

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 生物化學
  • 分子生物學
  • 電腦科學應用
  • 計算機理論與數學
  • 計算數學

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