title: Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data.
abstract: BACKGROUND
  Defining regulatory networks, linking transcription factors (TFs) to their targets, is a central problem in post-genomic biology. One might imagine one could readily determine these networks through inspection of gene expression data. However, the relationship between the expression timecourse of a transcription factor and its target is not obvious (e.g. simple correlation over the timecourse), and current analysis methods, such as hierarchical clustering, have not been very successful in deciphering them.
  
  
  RESULTS
  Here we introduce an approach based on support vector machines (SVMs) to predict the targets of a transcription factor by identifying subtle relationships between their expression profiles. In particular, we used SVMs to predict the regulatory targets for 36 transcription factors in the Saccharomyces cerevisiae genome based on the microarray expression data from many different physiological conditions. We trained and tested our SVM on a data set constructed to include a significant number of both positive and negative examples, directly addressing data imbalance issues. This was non-trivial given that most of the known experimental information is only for positives. Overall, we found that 63% of our TF-target relationships were confirmed through cross-validation. We further assessed the performance of our regulatory network identifications by comparing them with the results from two recent genome-wide ChIP-chip experiments. Overall, we find the agreement between our results and these experiments is comparable to the agreement (albeit low) between the two experiments. We find that this network has a delocalized structure with respect to chromosomal positioning, with a given transcription factor having targets spread fairly uniformly across the genome.
  
  
  BACKGROUND
  The overall network of the relationships is available on the web at http://bioinfo.mbb.yale.edu/expression/echipchip
authors.affiliation: Department of Ophthalmology, Johns Hopkins Medical School, Baltimore, MD 21287, USA.
authors.forename: Jiang
authors.initials: J
authors.lastname: Qian
authors.forename: Jimmy
authors.initials: J
authors.lastname: Lin
authors.forename: Nicholas M
authors.initials: NM
authors.lastname: Luscombe
authors.forename: Haiyuan
authors.initials: H
authors.lastname: Yu
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D018390:Gene Targeting / Q000379:methods; D016678:Genome; D008954:Models, Biological; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D014157:Transcription Factors / Q000378:metabolism*; D014584:User-Computer Interface
chemical_list: D020543:Proteome; D014157:Transcription Factors