Research On A Community Detection Method Of Drug-disease Co-target Interaction Network For Alzheimer’s Disease

Alzheimer’s disease is a polyneurodegenerative disease that is the leading cause of dementia in the elderly population.Worldwide,nearly 37 million people have been affected.Presumably,as the cause of death in older adults,Alzheimer’s disease will rank as the third leading cause of death in the coming years after cancer and heart disease.Although traditional drugs used to treat Alzheimer’s disease can slow the course of the disease and relieve symptoms,they cannot ultimately cure the disease.The pathogenesis of Alzheimer’s disease is complex and inconclusive,and its pathogenesis and related targets need to be further explored.Community detection is a common network analysis method,and the drug-disease co-target interaction network community discovery algorithm can find some targets with similar characteristics in the drug-disease co-target network,which can help further reveal the potential interaction mechanism and interaction relationship between drugs and diseases,and provide useful reference and guidance for clinical treatment.Such as predicting drug efficacy,side effects and drug resistance,discovering drug combinations with synergistic effects,improving treatment effects and reducing side effects.The traditional target interaction network community discovery algorithm is only based on the topology of the network,and fails to consider the edge information between nodes.Based on the above theory,this paper proposes a Community Detection Method for drug-disease co-target interaction network for Alzheimer’s disease by Fusing Edge information and markov clustering algorithm(CDMFE).Through the four databases of Gene Cards,Drug Bank,Therapeutic Target Database and Swiss Target Prediction,the disease targets of Alzheimer’s disease and the targets of some related drugs are collected,and the edge information between nodes is fused to construct a drug-disease co-target interaction network based on Alzheimer’s disease.Considering the topology of the links in the graph,we convert the edges of the graph into the nodes of the line graph,measure the similarity between the nodes of the line graph through Manhattan distance,Jaccard similarity,Pearson correlation coefficient and cosine similarity,and obtain the similarity matrix,and then use the CDMFE method based on Markov clustering for community detection.In the drug-disease co-target interaction network based on Alzheimer’s disease,the effectiveness of the proposed community detection method integrating edge information and Markov clustering is tested and verified,and the results show that the proposed algorithm performs well on extended modularity(EQ),community frequency(CF)and Benjamini score indicators.By investigating and screening high-risk populations,community-based discovery approaches can facilitate early detection and diagnosis,delay disease progression,and improve treatment outcomes.In addition,the method will help to collect a large amount of clinical data,promote Alzheimer’s disease related research,deepen the knowledge and understanding of the disease,and provide strong support for the development of more effective treatments.Therefore,it is hoped that the community discovery method proposed in this paper can promote the progress of Alzheimer’s disease treatment and drug development,and make important contributions to academic and clinical research in this field.