Using FunCoup to predict disease genes
Eric Sonnhammer *
Build networks combining all biological data PPI, coexpression, phylogenetic profiles (for co-evolution), domain interactions, subcellular co-localisation, TF binding sites, miRNA targetting.
Uses orthology to relate between organisms. Data is compared vs "training sets" in a Bayesian Framework. Using Gold Standard datasets HPRD for PPI, KEGG metabolic and signalling pathways. Uses log odds ratios. Networks cut at a confidence of 0.2 number of interactions grows exponentially with confidence level (why does this worry me?). Lots of support for interations comes from orthologous species.
Query from a few starting genes. Searches across species show orthologues. Examples for Parkinsons and Alzheimers from his paper.
The methods can only predict associations to disease if the genes are part of the Large Central Component - isolated genes are not within the network.