Figure 1. Transitive chemical-disease inferences and the computational approaches used to score inferences.

A) Diagram of local network for the transitive chemical-disease inference (dotted line) between a chemical, X, and a disease, Y, using a set of genes, A, that have both curated chemical-gene interactions and gene-disease associations (solid lines). The chemical, disease and each gene involved have interactions and relationships to other nodes (chemicals, genes, diseases) in the database. Chemical X has some number of other genes (grey circles) that it interacts with and associated diseases (grey squares). Disease Y has other associated genes and curated relationships to other chemicals (grey triangles). Each gene used to make the inference, g₁ to g_n, are known to interact with other chemicals (grey triangles) and are associated with other diseases (grey squares). B) Diagrams showing three methods to score inferences. The first, C_XY and p₁, is based on the number of genes (circles) used to make the inference and the connectivity (bold lines) of the chemical (triangle) and disease (square). The second, p₂, takes the number of genes (circles) used to make the inference and their connectivity (bold lines) into account. The third, S_XYA and W_XYA, takes the number of genes into account as well as the connectivity of the chemical, disease and each of the genes into account.