Research On Pathogenetic Complex Detection Algorithms Based On Weighted Differential Network

One of the most popular topics in biomedical research is the systematic analysis and comprehensive understanding of the molecules in cells and the law of life through their interactions.Protein,as one of the various molecules in cells,is an integral part of the organizational structure of the organism,and is an indispensable material undertaking for the biological activities of life.However,these proteins do not exist in the body,but by other proteins together with the composition of different complexes,regulating the normal physiological processes of the control cells and the pathological process.All of the protein interactions in a living body can constitute a protein interaction network(PPI network).The identification of complexes from PPI networks is of great importance in predicting the function of proteins and explaining important biological processes.Protein complex recognition algorithm is diverse,the accuracy of recognition and the quality of the results are also basically mature.However,recognition of pathogenic complexes for specific diseases is still at an early stage.Complex diseases have a serious impact on human work and life.The occurrence and development of complex diseases is often not the result of single gene mutation,but the complex relationship between genes and the results of the interaction,and protein complex is the basic function of the protein unit.Therefore,it is of practical significance to study protein complexes associated with complex diseases.The existing method of protein complex recognition is mainly based on the method of static protein interaction network and the method based on dynamic protein interaction network,which is divided from the type of PPI network used.However,these networks are unable to reflect the difference between the patient and the complex in the normal PPI network.At present,the difference network research has become a hot research at home and abroad.Through the analysis of the current situation of the research on the current differences,it is found that the previous research mainly focused on the construction of the differential network,and the research on the recognition of functional complexes in the different networks is relatively few.Differentiated networks have the advantage that traditional PPI networks cannot be substituted in the study of pathogenic complexes of complex diseases.Therefore,the identification algorithm of pathogenic complex based on differential network needs to be studied urgently.This paper builds a network of weighted differences based on protein networks to identify pathogenic complexes.First,the normal and diseased genes were extracted from the protein network by protein network data and normal and diseased gene expression data.The interaction between normal and diseased samples(correlation coefficient greater than specific threshold)was obtained by correlation coefficient screening The relationship between the two networks based on the difference between the two proteins is more likely to be associated with disease-related relationships to construct differential networks.Second,according to the GO comment information in the gene,the weights are assigned to each edge in the differentiated network to obtain the weighted difference network.Finally,the seed node expansion algorithm is used to extend the nodes to obtain complexes associated with complex diseases.The results show that the complexes identified in this paper play an important role in the development and progression of cancer.At the same time,the results show that this paper suggests that the compounds identified in this paper are important in the development and progression of cancer.The identification algorithm based on weighted difference network is an effective complex disease identification algorithm.In this paper,the deletion of unexpressed genes by introducing differential networks effectively improves the recognition accuracy of protein complexes,and the addition of gene annotation data makes the proteins with similar functions together well and further improves the prediction accuracy.However,there are many attributes of protein that are not considered in this paper.Therefore,the addition of more and more comprehensive protein properties to construct dynamic banding networks in order to dig deeper into disease-related complexes will be the focus of future research.