Research On Technologies Of Mining Disease Micrornas Based On Biological Network

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Since we entered the 21st Century, the research on non-coding RNA has been voted consecutively as top ten scientific breakthroughs for several years, and it won the Nobel Prize in Physiology or Medicine in 2006. MicroRNA is an important class of non-coding RNA, and is closely associated with the development of human diseases. Disease microRNAs can be identified by biological experiments, but it is often expensive and time-consuming. In this dissertation, we proposed several technologies for mining disease microRNAs based on bioinformatics, which aim at identifying the most possible microRNAs that potentially cause disease development. The proposed methods will drive testable hypotheses for the experimental efforts to identify the true roles of microRNAs in human diseases and provide a basis for the drug development, clinical diagnosis and treatment. The main contents include:(1) Mining and analyzing known disease microRNAsSince 2002, Accumulating studies have shown that microRNA deregulation contributes to the development of disease, detailed information on these known microRNA–disease relationships are scattered in literatures and there is no online repository for these known microRNA–disease relationships. Researchers are difficult to obtain these known microRNA-disease associations. Therefore, we develop a manually curated database entitled'miR2Disease', which provides a comprehensive resource of microRNA deregulation in various human diseases and manages these data. By analyzing these data, we found that some disease often share similar pathogenesis. In addition, we found three types of mechanisms that can explain the deregulation of disease microRNAs: First, microRNA is often located in disease-related loci, for example, minimal regions of loss of heterozygosity, minimal amplicons, or breakpoint fragile regions; Secondly, microRNA dereguation is caused by abnormal epigenetic modifications; such as DNA methylation, histone abnormal modification, etc.; Third, microRNA deregulation may be caused by abnormalities of the enzymes that are involved in microRNA biogenesis.(2) An algorithm for identifying disease microRNAs based on Boolean network is proposedBiological networks have played an important role in mining protein-coding disease genes. However, In the field of disease microRNA identification, no biolgocial network-based approach was proposed to mine the disease microRNAs. Therefore, we for the first constructed a Boolean functionally related microRNA network. By analyzing the network, we found that microRNA network is like other biological network whose degree follows the power distribution and is of the hierarchical organization of modularity. We further constructed a phenome-microRNAome network. In this network, we analyzed the known microRNA-disease associations and found that the deregulation of functionally related microRNAs tend to cause phenotypically similar diseases. Based on this point, we for the first proposed an algorithm for mining disease microRNAs based on Boolean biological network, and verified its validity.(3) An algorithm for identifying disease microRNAs based on weighted network is proposedTo take full advantage of silencing score between microRNA and its target gene and phenotypical similarity score, we proposed an algorithm for identifying disease microRNAs based on weighted network. Experimental results showed that the algorithm for identifying disease microRNAs based on the weighted network outperformed the approach based on Boolean Network.(4) An algorithm for identifying disease microRNAs based on support vector machine is proposedIn order to predict disease microRNAs directly from data, we translated the identification of disease microRNA into a classification problem, and proposed a method to predict disease microRNA based on support vector machine. we for the first introduced data mining, machine learning into the identification of disease microRNAs. Cross-validation results proved that the method is cost-effective.(5) Identifying disease microRNAs based on data fusionStatistics show that biomedical data obtained in recent three years are more than total ones obtained in the past fourty thousand years. The data grow explosively. Facing the vast ocean of biological data, how we tranlated this mass of data into meaningful medical diagnosis and treatment information and benefit the health of human beings. It is the great challenges that biomedical informatics faces in the 21st century. In this dissertation, we integrated a variety of biological data resources to construct a genome-wide functionally related gene network. Based on this network, we proposed an approach to predict disease microRNA by the use of the functional associations between the microRNA target gene and the known causing genes that cause the disease of interest. The proposed approach is applied to the colon cancer and is proved to be effective.