Research On Prediction Method Of Human Disease-related MicroRNA

Modern medical research showed that genes are the root cause of most human diseases.With the completion of the Human Genome Project,Researchers have transitioned from genome-wide sequencing to in-depth studies of genomic function and its regulatory mechanisms.That is,reaserachers found the mystery of human disease from the regulatory function of the genetic level.Because only 3-5% of the entire human genome can encode the protein DNA sequence.The vast majority of DNA sequences are only transcribed into RNA,but they do not participate in the direct coding of proteins.Therefore,the role of non-coding RNA-regulated gene expression appears gradually and becomes a new research hotspot in life science.Among them,microRNA in noncoding RNAs is widely studied by researchers.Micro RAN is a set of short non-coding RNA with a length of about 20-24 nucleotides.It is not involved in coding proteins,but play a critical regulatory role in determining protein-encoded DNA expression.Accumultaing studies have demonstrated that microRNA participates in all important aspects of biological processes.Furthermore,the abnormal expression of microRNA is one of important causes which result in the occourence and development of various diseases.In this thesis,we are committed to constructing microRNA functional network based on bioinformatics methods,and mining hidden associated information in network by network model,and researching on the disease-related microRNA,and then provide the necessary guarantee of biological experiments for biologists.It will have a tremendous contribution to the diagnosis and treatment of human diseases and the promotion of the development of gene drugs.The creative work mainly consists of the following three parts.(1)The method of accurately calculating the functional similarity of microRNA pair is proposed for constructing microRNA functional network.Accurate calculation of functional similarity of microRNA pair is a prerequisite for constructing microRNA functional network.It is known that microRNA with similar is normally associated with similar diseases and vice versa.Therefore,a method for calculating the functional similarity of microRNA pair,MFSS,is proposed by accurately measuring the semantic similarity of two groups of diseases associated with microRNA pair.First,we construct the directed acyclic graph(DAG)on disease terms according to the information content of disease terms in the national medical library Mesh.Second,we accurately calculate the information content of disease terms as its semantic contribution on the basis of the number of descendants of each disease term to improve the original calculation method on information content of disease terms.Third,the semantic similarity of the two diseases is calculated according to the information content of the disease term,and then the semantic similarity of the two groups of two microRNA-associated diseases is achieved.Finally,the semantic similarity of the two groups of diseases is converted to functional similarity of microRNA pair,and we construct microRNA functional network.In addition,the effeciency of the method is inferred by microRNA in the same family,microRNA in the same cluster and that of microRNA that belong to neither the same family nor the same cluster.MFSS is proved that it has higher performance than the existing microRNA function similarity calculation method for 15 kinds of human diseases.(2)On the basis of constructing microRNA functional network,a method based on random walk is proposed for predicting disease-related microRNA.We construct microRNA functional network on the basis of the accurately calculating microRNA functional similarity for converting the prediction of disease-related microRNA into random walk problem.A new prediction method,MIDP,based on random walk on the functional network is proposed in the thesis.The prior information of nodes in the microRNA functional network can be completely exploited in the method.For the diseases with some known related mi RNAs,the network nodes are divided into marked nodes that are known to be associated with a specific disease and unmarked nodes have not been found to be associated with a specific disease,and the transition matrices are established for the two categories of nodes by combining the different transition weights of the two categories of nodes on the basis of the functional similarity of microRNA pair.A random walker starts at one of known specific disease-related marked nodes with equal probability and random walk in the functional network according to the transition matrices.After the iterative walking process is converged,the steady-state probability with which the walker stays at a candidate node is defined as its relevance score between the specific disease and the microRNA node.In this way,the unmarked nodes with higher scores are more likely to be used as microRNA candidates associated with the specific disease.The efficiency of MIDP is compared with other prediction methods by the association data of 15 human diseases.The experimental result indicates that our methods can effectively predict microRNA candidates associated with specific diseases and achieve superior prediction performance.(3)On the basis of constructing microRNA-disease bilayer network,a method based on non-negative matrix factorization is proposed for predicting disease-related microRNA.Currently,disease-related microRNA prediction methods relied on microRNA that have already been related to the specific disease and therefore are not effective on the new diseases without any known related microRNA.We construct a bilayer network to represent the complex relationships among microRNA,among diseases and between microRNA and diseases,and propose a prediction method of disease-related microRNA based on non-negative matrix factorization with sparse constraints.The method is referred to as DMPred.The method integrates the microRNA functional similarity,the disease similarity,and the microRNA-disease associations seamlessly,which exploits the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information.Considering the correlation between the candidates related to various diseases,it predicts their respective candidates for all the diseases simultaneously.In addition,the sparseness characteristic of disease-microRNA was introduced to generate more reliable prediction model.The results of cross validation and simulation experiments on 15 common diseases confirmed the superiority of DMPred for the specific disease-related microRNA and the new diseases without any known related microRNA.