title: Gene expression trends and protein features effectively complement each other in gene function prediction.
abstract: BACKGROUND
  Genome-scale 'omics' data constitute a potentially rich source of information about biological systems and their function. There is a plethora of tools and methods available to mine omics data. However, the diversity and complexity of different omics data types is a stumbling block for multi-data integration, hence there is a dire need for additional methods to exploit potential synergy from integrated orthogonal data. Rough Sets provide an efficient means to use complex information in classification approaches. Here, we set out to explore the possibilities of Rough Sets to incorporate diverse information sources in a functional classification of unknown genes.
  
  
  RESULTS
  We explored the use of Rough Sets for a novel data integration strategy where gene expression data, protein features and Gene Ontology (GO) annotations were combined to describe general and biologically relevant patterns represented by If-Then rules. The descriptive rules were used to predict the function of unknown genes in Arabidopsis thaliana and Schizosaccharomyces pombe. The If-Then rule models showed success rates of up to 0.89 (discriminative and predictive power for both modeled organisms); whereas, models built solely of one data type (protein features or gene expression data) yielded success rates varying from 0.68 to 0.78. Our models were applied to generate classifications for many unknown genes, of which a sizeable number were confirmed either by PubMed literature reports or electronically interfered annotations. Finally, we studied cell cycle protein-protein interactions derived from both tandem affinity purification experiments and in silico experiments in the BioGRID interactome database and found strong experimental evidence for the predictions generated by our models. The results show that our approach can be used to build very robust models that create synergy from integrating gene expression data and protein features.
  
  
  BACKGROUND
  The Rough Set-based method is implemented in the Rosetta toolkit kernel version 1.0.1 available at: http://rosetta.lcb.uu.se/
authors.affiliation: Department of Plant Systems Biology, VIB Technologiepark 927, 9052 Gent, Belgium. krwab@psb.ugent.be
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Wabnik
authors.forename: Torgeir R
authors.initials: TR
authors.lastname: Hvidsten
authors.forename: Anna
authors.initials: A
authors.lastname: Kedzierska
authors.forename: Jelle
authors.initials: J
authors.lastname: Van Leene
authors.forename: Geert
authors.initials: G
authors.lastname: De Jaeger
authors.forename: Gerrit T S
authors.initials: GT
authors.lastname: Beemster
authors.forename: Jan
authors.initials: J
authors.lastname: Komorowski
authors.forename: Martin T R
authors.initials: MT
authors.lastname: Kuiper
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D030562:Databases, Protein; D015870:Gene Expression; D020869:Gene Expression Profiling; D016678:Genome; D023281:Genomics; D011506:Proteins / Q000737:chemistry / Q000235:genetics*
chemical_list: D011506:Proteins