Research On Prediction Methods Of Components Related To Cancer-Pathway And Pathway Visualization Implementation

Complex diseases,such as cancer,have always been important factors that seriously harm human health.With the opening and development of the post-genome era in humans,a large number of high-throughput sequencing technologies have been continuously developed,making biomedical research gradually free from the constraints of traditional research methods.On this basis,research methods relying on big data have also been proposed,which have accelerated the research process of various complex diseases in the biomedical field.The research methods have shifted gradually from single gene analysis to finding genes or biological pathways that are dysregulated in cellular processes.Biological pathways play an important role in human life activities,and their minor changes may cause certain cancers to some extent.Bio-pathway analysis has become an important direction in cancer researches.At present,the commonly used cancer pathway recognition technologies are mainly divided into two categories: gene enrichment analysis-based and network-based pathway recognition methods.These two methods seek the relationships between cancers and pathways based on the gene functions and network structures of pathways.Biological pathway expansion is an important method of cancer pathway analysis,mainly to find emerging possible pathogenic genes by studying genes closely related to pathways in human gene networks,which is very important for exploring the mechanisms of cancer development,finding drug targets,and early prediction of cancer.On this basis,this paper proposes a prediction method for cancer-pathway related components based on network expansion technology.Firstly,the gene co-expression network under breast cancer data is constructed as the global network of the algorithm,and its topology is analyzed from both global and local perspectives.Then,the selected pathway gene sets which are significantly correlated with the current phenotype are embedded into the global network and the seed networks are extracted therefrom.Finally,based on the related network expansion algorithm,a biological network extension algorithm(WPEA)based on weighted network is proposed.And the algorithm integrates the multi-topological characteristics of the network to expand all seed networks in the global network,which achieves an extension of each pathway under the current phenotype,and the results of its experiments are compared and analyzed.It was found that compared with the original algorithm,our algorithm can effectively identify the genes related to the current phenotype and pathway for all the original pathways,and at the same time identify more genes related to the cancer pathway,and the prediction effect is better.As a large number of biological experiments have been carried out,the biological pathway data have been accumulated,which have increased the demand for visualization.This project has built a editable biological pathway visualization and analysis system.This system uses the network representation and the pathway representation to visualize biological pathways,satisfying their different requirements for visualization in computer science and biomedicine.It also enables cross-inquiry between several different biological entities of genes,pathways,diseases,and drugs,centered on genes and pathways.At the same time,it can realize multiple functions such as editable and layout adjustment.