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STRING PPI Score to Characterize Protein Subnetwork Biomarkers for Human Diseases and Pathways

Protein sub network biomarkers for 144 diseases and pathways are analyzed in terms of protein-protein interaction (PPI) score available in STRING database. Most of the sub network biomarker (SNB) studies are to classify disease samples from the control. But no de novo algorithm is available to identify SNB from the whole genome PPI network without the knowledge of differentially expressed genes. Recently, based on mouse model, researchers showed that there exists a dynamical network biomarker which can distinguish among the normal state, pre-disease state, and disease state of a disease progression. But, most of the gene expression data for human diseases are at the disease state. No data is available for the first two stages of a disease. Understanding the network behavior of a disease at disease state might help in the development of de novo algorithm for predicting protein SNBs not only for disease state but also for early stages of a disease or early warning signals. PPI score in STRING database represents a rough estimate of how likely a given interaction describes a functional linkage between two proteins. So, analyzing protein SNB for human diseases at disease state with respect to PPI score may shed some light in the development of de novo models for predicting SNB. A simple brute force approach is used to isolate the SNB for a disease or pathway from the genome-wide PPI network by projecting the corresponding differentially expressed proteins. Then the SNBs are analyzed in terms of PPI score. Our investigation shows that higher is the PPI score of a network is more likely to produce a true SNB for a disease. Results also show that Physical PPIs with high score are more capable of producing a true SNB.