Prioritising risk pathways of complex human diseases based on functional profiling.

Analysis of the biological pathways involved in complex human diseases is an important step in elucidating the pathogenesis and mechanism of diseases. Most pathway analysis approaches identify disease-related biological pathways using overlapping genes between pathways and diseases. However, these approaches ignore the functional biological association between pathways and diseases. In this paper, we designed a novel computational framework for prioritising disease-risk pathways based on functional profiling. The disease gene set and biological pathways were translated into functional profiles in the context of GO annotations. We then implemented a semantic similarity measurement for calculating the concordance score between a functional profile of disease genes and a functional profile of pathways (FPP); the concordance score was then used to prioritise and infer disease-risk pathways. A freely accessible web toolkit, 'Functional Profiling-based Pathway Prioritisation' (FPPP), was developed (http://bioinfo.hrbmu.edu.cn/FPPP). During validation, our method successfully identified known disease-pathway pairs with area under the ROC curve (AUC) values of 96.73 and 95.02% in tests using both pathway randomisation and disease randomisation. A robustness analysis showed that FPPP is reliable even when using data containing noise. A case study based on a dilated cardiomyopathy data set indicated that the high-ranking pathways from FPPP are well known to be linked with this disease. Furthermore, we predicted the risk pathways of 413 diseases by using FPPP to build a disease similarity landscape that systematically reveals the global modular organisation of disease associations.