Abstract
In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.
Highlights
The new emerging paradigm of network medicine has been dramatically changing the way we define and analyze human diseases
We combined the topological properties of the human interactome with disease information derived from SWIMbased correlation network analysis
Identification of disease-specific switch genes The SWItch Miner (SWIM) algorithm was applied to a specific group of diseases of healthcare, since promoting differentiation and restraining tumor interest to build disease-specific Gene expression networks (GENs) (Supplementary Data 1) and growth may support rational, personalized planning of disease extract a list of switch genes for each disease through an accurate prevention or treatment
Summary
The new emerging paradigm of network medicine has been dramatically changing the way we define and analyze human diseases. The two key issues that each network-based algorithm has to address are the identification of the critical entities in the system under investigation (nodes), and the nature of the interactions between these entities (edges) This information depends on the study design, the phenotype under investigation, the biological question of interest, the molecular entities measured, and the type and size of the available datasets. Tools developed within the field of network medicine are highly customized to leverage these biomedical data with respect to the given biological or disease context Several of these algorithms[5,6,7] make use of the human protein–protein interaction (PPI) network, denoted the human interactome, which is a network of proteins (nodes) in which the edges are the physical and functional interactions occurring between them. The basic premise of this exercise is that, even though correlation is not causation, co-expressed genes are functionally coordinated in response to an external stimulus, implying that they may be part of the same complexes or pathways, and may influence each other or may be influenced by the same underlying mechanism(s)
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