title: Addressing false discoveries in network inference.
abstract: BACKGROUND
  Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles.
  
  
  RESULTS
  We find that methods exploiting known targets show an unexpectedly high rate of false discoveries. This leads to inflated performance estimates and the prediction of an excessive number of new interactions for regulators with many known targets. These issues are hidden from common evaluation and cross-validation setups, which is due to Simpson's paradox. We suggest a confidence score recalibration method (CoRe) that reduces the false discovery rate and enables a reliable performance estimation.
  
  
  CONCLUSIONS
  CoRe considerably improves the results of network inference methods that exploit known targets. Predictions then display the biological process specificity of regulators more correctly and enable the inference of accurate genome-wide regulatory networks in eukaryotes. For yeast, we propose a network with more than 22 000 confident interactions. We point out that machine learning approaches outside of the area of network inference may be affected as well.
  
  
  METHODS
  Results, executable code and networks are available via our website http://www.bio.ifi.lmu.de/forschung/CoRe.
  
  
  BACKGROUND
  robert.kueffner@helmholtz-muenchen.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Ludwig-Maximilians-Universität München, Institut für Informatik, Munich, Germany and.
authors.forename: Tobias
authors.initials: T
authors.lastname: Petri
authors.affiliation: Ludwig-Maximilians-Universität München, Institut für Informatik, Munich, Germany and.
authors.forename: Stefan
authors.initials: S
authors.lastname: Altmann
authors.affiliation: Ludwig-Maximilians-Universität München, Institut für Informatik, Munich, Germany and.
authors.forename: Ludwig
authors.initials: L
authors.lastname: Geistlinger
authors.affiliation: Ludwig-Maximilians-Universität München, Institut für Informatik, Munich, Germany and.
authors.forename: Ralf
authors.initials: R
authors.lastname: Zimmer
authors.affiliation: Ludwig-Maximilians-Universität München, Institut für Informatik, Munich, Germany and Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
authors.forename: Robert
authors.initials: R
authors.lastname: Küffner
mesh_terms: D000465:Algorithms; D005189:False Positive Reactions; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D000069550:Machine Learning; D020411:Oligonucleotide Array Sequence Analysis; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology / Q000379:methods*
chemical_list: D029701:Saccharomyces cerevisiae Proteins