Abstract

Accurate disease module is helpful in understanding the molecular mechanism of disease causation and identifying drug target. However, for the fragmentization of disease module in incomplete human interactome, how to determine connectivity pattern and detect a full neighbourhood of disease is an open problem. In this paper, a topology-based method is developed to dissect the connectivity of intermediate nodes and edges and form a succinct disease module. By applying this Connect separate Connected Components (CCC, C3) method on a large corpus of curated diseases, we find that most Separate Connected Components (SCCs) formed by Disease-Associated Proteins (DAPs) can be connected into a well-connected component as a succinct observable module. This pattern also holds for altered genes from multi-omics data such as The Cancer Genome Atlas. Overall, C3 tool can not only inspire a deeper understanding of interconnectedness of phenotypically related genes and different omics data, but also be used to detect a well-defined neighbourhood that drives complex pathological processes.