title: Ranking transitive chemical-disease inferences using local network topology in the comparative toxicogenomics database.
abstract: Exposure to chemicals in the environment is believed to play a critical role in the etiology of many human diseases. To enhance understanding about environmental effects on human health, the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) provides unique curated data that enable development of novel hypotheses about the relationships between chemicals and diseases. CTD biocurators read the literature and curate direct relationships between chemicals-genes, genes-diseases, and chemicals-diseases. These direct relationships are then computationally integrated to create additional inferred relationships; for example, a direct chemical-gene statement can be combined with a direct gene-disease statement to generate a chemical-disease inference (inferred via the shared gene). In CTD, the number of inferences has increased exponentially as the number of direct chemical, gene and disease interactions has grown. To help users navigate and prioritize these inferences for hypothesis development, we implemented a statistic to score and rank them based on the topology of the local network consisting of the chemical, disease and each of the genes used to make an inference. In this network, chemicals, diseases and genes are nodes connected by edges representing the curated interactions. Like other biological networks, node connectivity is an important consideration when evaluating the CTD network, as the connectivity of nodes follows the power-law distribution. Topological methods reduce the influence of highly connected nodes that are present in biological networks. We evaluated published methods that used local network topology to determine the reliability of protein-protein interactions derived from high-throughput assays. We developed a new metric that combines and weights two of these methods and uniquely takes into account the number of common neighbors and the connectivity of each entity involved. We present several CTD inferences as case studies to demonstrate the value of this metric and the biological relevance of the inferences.
authors.affiliation: Mount Desert Island Biological Laboratory, Salisbury Cove, Maine, United States of America.
authors.forename: Benjamin L
authors.initials: BL
authors.lastname: King
authors.forename: Allan Peter
authors.initials: AP
authors.lastname: Davis
authors.forename: Michael C
authors.initials: MC
authors.lastname: Rosenstein
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Wiegers
authors.forename: Carolyn J
authors.initials: CJ
authors.lastname: Mattingly
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions / Q000235:genetics*; D053263:Gene Regulatory Networks; D006801:Humans; D025941:Protein Interaction Mapping; D043922:Toxicogenetics / Q000379:methods*