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A Weighted Association Rule Mining Method for Predicting HCV-Human Protein Interactions

机译：用于预测HCV-人蛋白质相互作用的加权关联规则挖掘方法

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Background: Hepatitis C Virus causes the most severe form of chronic liver disease and nearly 200 million people worldwide are estimated to be infected with this virus. Much about the HCV pathogenesis process is still unknown. The study of interactions between HCV and human proteinswill lead to deeper understanding of HCV mechanism. Objective: The objective of this paper is to predict potentially new HCV-Human protein interactions using a weighted association rule mining technique. Methods: A new computational method was developed for mining associations withina bipartite graph that was constructed from the HCV-human protein interactions dataset. A new mathematical model was applied to weigh the discovered association rules based on Gene Ontology annotations of viral and human proteins. HCVpro database was used to generate a human-viral bipartitegraph that was then analyzed computationally to extract biclusters within the graph. Association rules were extracted from the bipartite graph and weighted using a mathematical model that incorporated information about proteins available from Gene Ontology knowledge base. Results: Fortytwo new interactions between HCV and human proteins were predicted. Some of these predicted interactions were validated through literature survey and enrichment studies such as Gene ontology-based analysis, pathway- based analysis and disease association based analysis. Conclusion: Themethodology developed in this paper can also be used for various other kind of data analysis and hence it carries a wide scope. This will be useful to conduct similar kind of experiments for other disease databases.
机译：背景：丙型肝炎病毒导致最严重的慢性肝病形式，估计全世界近2亿人感染这种病毒。关于HCV发病机制过程仍然未知。 HCV与人类蛋白质之间的相互作用的研究导致对HCV机制的更深理解。目的：本文的目的是使用加权关联规则采矿技术预测潜在的新HCV-人蛋白质相互作用。方法：开发了一种新的计算方法，用于从HCV-人蛋白相互作用数据集构建的二分拉内的挖掘联合图。应用了一种新的数学模型来称量基于病毒和人蛋白的基因本体注释的发现的关联规则。 HCVPro数据库用于生成人类病毒性二兆跳换器，然后计算地分析，以在图中提取双板。从二角形图中提取关联规则，并使用数学模型加权，该模型结合了有关基因本体知识库可获得的蛋白质的信息。结果：预测HCV和人蛋白质之间的新相互作用。通过文献调查和富集研究验证了一些这些预测的相互作用，例如基于基于基于基于基于基于基于基于基于基于基于基于本底属性的分析和疾病的分析。结论：本文开发的Themethodology还可以用于各种其他类型的数据分析，因此它带来了广泛的范围。这对于对其他疾病数据库进行类似的实验将是有用的。

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