title: Data-driven ontologies.
abstract: Gene networks are important tools in studying gene-gene relationships and gene function. Understanding the relationships within these networks is an important challenge. Ontologies are a critical tool in helping deal with these data. The use of the Gene Ontology, for example, has become routine in methods for validation, discovery, etc. Here we present a novel algorithm that synthesizes an ontology by considering both extant annotation terms and also the connections between genes in gene networks. The process is efficient and produces easily inspectable ontologies. Because the relationships drawn between terms are heavily influenced by data, we call these "Data-Driven" Ontologies. We apply this algorithm to both discover new relationships between biological processes and as a tool to compare sets of genes across microrarray experiments. Supplemental data and source code are available at: http://www.ddont.org
authors.affiliation: School of Informatics, Indiana University, Bloomington IN, 47405, USA. jccostel@indiana.edu
authors.forename: James C
authors.initials: JC
authors.lastname: Costello
authors.forename: Dan
authors.initials: D
authors.lastname: Schrider
authors.forename: Jeff
authors.initials: J
authors.lastname: Gehlhausen
authors.forename: Mehmet
authors.initials: M
authors.lastname: Dalkilic
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012441:Saccharomyces cerevisiae / Q000235:genetics