Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to predict novel d rugs for HCC based on multi-source random walk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a gene-gene weighted i nteraction network (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank = 9 in top-20 list) is proven to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And, it has 11 overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.
With the rapid growth of data, limited by the storage capacity, more and more IoT applications choose to outsource data to Cloud Service Providers (CSPs). But, in such scenarios, outsourced data in cloud storage can be easily corrupted and difficult to be found in time, which brings about potential security issues. Thus, Provable Data Possession (PDP) protocol has been extensively researched due to its capability of supporting efficient audit for outsourced data in cloud. However, most PDP schemes require the Third-Party Auditor (TPA) to audit data for Data Owners (DOs), which requires the TPA to be trustworthy and fair. To eliminate the TPA, we present a Public Mutual Audit Blockchain (PMAB) for outsourced data in cloud storage. We first propose an audit chain architecture based on Ouroboros and an incentive mechanism based on credit to allow CSPs to audit each other mutually with anticollusion (any CSP is not willing to help other CSPs conceal data problems). Then, we design an audit protocol to achieve public audit efficiently with low cost of audit verification. Rigorous analysis explains the security of PMAB using game theory, and performance analysis shows the efficiency of PMAB using the real-world dataset.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.