Output details

Gene function prediction using semantic similarity clustering and enrichment analysis in the malaria parasite Plasmodium falciparum

<28>Although functional genomics techniques promise to revolutionise diagnostics and treatments for disease, they are limited by the quality and volume of often disparate data sources. We develop a new Bayesian learning algorithm to combine data from eight heterogeneous sources; then use semantic similarity metrics (for the combined data) to cluster genes of similar function, leading to new predictions about specific malaria genes. Worldwide uptake of our online tool http://fbs3pcu112.leeds.ac.uk/~bio5pmrt/PAGODA/ by the malaria research community includes the Malaria Centre at LSHTM (D.Baker), who is using our approach to identify gene targets, with significant expected impact on mitigation of this major disease.