Rule mining on microRNA expression profiles for human disease understanding

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This research employs rule mining methods to study the important roles of miRNAs in human diseases. From past experience and from reviewing the literature, rule mining is a widely used data mining technique for the discovery of interesting relationships in large data sets. MicroRNAs (miRNAs) are endogenous and highly conserved non-coding RNA molecules. They can inhibit and/or promote the post-transcriptional expression of target messenger RNAs (mRNAs). miRNAs thus play a pivotal role in a cell’s differentiation, proliferation, growth, mobility, and apoptosis, as well as in viral replication and proliferation. This has inspired many research works aimed at detecting miRNAs’ functions in human disease. However, with the current deluge of miRNA data, previous works have suffered from limitations in terms of handling the relationship between various molecules. Firstly, they usually identify single miRNAs as biomarkers, and always produce low sensitivity and specificity. Secondly, intensive research largely depends on the inverse expression relationships between miRNAs and mRNAs to discover miRNA-mRNA regulatory modules. Finally, the miRNA-miRNA co-regulations and miRNA self-regulations have not been well investigated. As a result, rule mining is a powerful new technology with great potential to help researchers focus on the most important miRNAs for understanding human diseases. This thesis reports our past and current research outcomes in this area. The contributions of the thesis are as follows: • A novel rule mining method is proposed to detect the significant miRNA biomarkers. • A “change to change” method is proposed to mine both positive and negative regulatory relationships from paired miRNA and mRNA expression data sets. • A progressive data refining approach is proposed to identify the lung cancer miRNA-miRNA co-regulation network. • A novel framework is proposed to detect the self-regulation miRNAs. The research was conducted through four case studies. (1) The first case study was on lung squamous cell carcinoma for accurate diagnosis of this disease through the reliable miRNA biomarkers identified by a novel rule discovery method. (2) The second case study was on paired miRNA and mRNA expression data of HCV patients to detect both positive and negative regulatory modules. (3) The third case study was on lung cancer data sets for the computational methods to identify miRNA-miRNA co-regulation networks and miRNA-miRNA co-regulatory relationships. (4) The fourth case study was on multiple data types to infer self-regulation miRNAs in humans through an integrative rule mining framework and approach. All the results have been verified by the existing literature and databases.
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