Research On Microarray Gene Expression Data Based On Bayesian Network

With the completion of human genome sequence sketches, functional genomics re-search plays more and more important part in the field of life science. Clarifying geneexpression regulation of selective on information and the molecular mechanism of in-teraction, and revealing the essence of life is the core problem and an important partof the study of functional groups. With the deepening of the research on genomics,gene expression studies in a single gene regulation has been extended to the regulation,linear three-dimensional level multiple genes and gene regulation of the whole genomeclusters. How to effectively use the existing data, fully integrate genomics of multi-disciplinary new ideas, establish a new test system and technical system, expound thegenome of the regulation network analysis and express the relationship between genesand functional genomics has become the focus of international competition within the?eld.Bayesian network method goes with the graph theory knowledge to probability,combining with graphical representation, causal relationship and uncertainty reasoningclearly, and it has good features, this paper will take bayesian network into microar-ray data analysis, it describes the relationship between genes with probability, whichillustrates the entire genome control network.This paper discusses the basic concept of bayesian network, development his-tory, classiffcation and its characteristics, and the bayesian network structure of somebasic methods, this paper expounds the bayesian network structure and parameters oflearning principle, and then puts forward the bayesian score study method, the struc- ture of the K2 algorithm and parameter study method to establish the mutual influencebetween genes in the bayesian network model. In the example, this paper carefully de-scribes a microarray data as a bayesian network model and the whole construction ofbayesian network. This gene expression data gets discretization after three values of thebayesian network construction. The paper constructs a bayesian network model andanalyses the function and influence between several nodes which have more children inthe network.This paper made several aspects of the research as follows:(1) For complete datas, we use the K2 algorithm in the bayesian network struc-ture learning. Because each node shoule be predetermined when using K2, we use De-cision Tree classic algorithm to complete sequencing problem and improve the learninge?ciency. The numbers of father nodes are discussed in the network learning process.At last by structure learning, we get a bayesian network model that can express therelationship between genes. Based on this, we use the Maximum Likelihood Estima-tion to estimate parameters, thereby mastering posteriori probabilities between genes.Finally we find the bayesian equivalence class, and we can reverse the arcs in the orig-inal network with a priori knowledge which can be obtained by means of experiments.Reversing the arcs does not affect the structure of networks, which is more practical.(2) If we have known a priori knowledge such as the network structure, we setrespectively random 1/3, 1/4, 1/5 loss value of the data, then use Expectation Maxi-mum algorithm for learning parameters to acquire the processing ability of ExpectationMaximum algorithm in datas containing missing values.(3) If we have no prior knowledge and the data contains loss values, we useStructural Expectation Maximum algorithm to complete the network structure learningand get the corresponding parameters, and then we obtain the handling ability ofStructural Expectation Maximum.Bayesian network has very good theoretical knowledge and clear knowledgeexpression of uncertainty, it is an important method in data mining, and plays an important role in datamine. Taking bayesian network into the microarray experimentdata analysis can better construct network models, analyze the interaction of in?uence,and it can be widely used in the study of biological and oncology to observe the diseasecaused by the change of gene expression.