Long Non-coding RNA And Environment Factors Associations Predictive Research

The interaction between genes and environmental factors is one of the main topics of bioinformatics research.With the deepening of research,it has been found that the role of non-coding RNA in genetic expression is more and more prominent The The association between long noncoding RNA and LncRNA and environmental factors is the focus of the study on the interaction between genes and EFs.However,due to lncRNA and environmental factors association research is still in its infancy,there is not yet a reliable lncRNA-EFs association relational database is effectively established.We hope that the association of lncRNA-EFs is predicted by several predictive methods of lncRNA-EFs association,and a reliable prediction method and prediction result are given,which can guide the research of lncRNA-EFs association.In this paper,two predictive methods are used to predict the association of lncRNA-EFs,which are based on the three-dimensional network resource propagation method and the restarted random walk method.In the method of resource transmission based on three-party network,the lncRNA-mi RNAs and miRNA-EFs association network are constructed into a three-way network through intermediate miRNA nodes.The association of lncRNAs and environmental factors is determined by resource propagation algorithm.In the method of walking,the data used are based on the data of the three-dimensional network resource-based method,and the chemical structure information of the environmental factors is added,and more accurate prediction results are obtained by adding more high-precision biological information.By comparing the prediction efficiency and prediction results of the two algorithms,the usability of the lncRNA-EFs association is predicted.The experimental results show that the three-way network resource propagation algorithm and the restart random walk algorithm can effectively predict the association of lncRNA-EFs,But the restarted random walk algorithm can improve the accuracy of the algorithm by integrating more reliable biological information.Finally,the predictions of the two algorithms are compared with each other,and an lncRNA-EFs association relational database is constructed.In order to quickly access the lncRNA-EFs association data,we have designed a dynamic web page through C # language,which can be easily connected to the database,and query and download the lncRNA-EFs association data.