A disease-gene association prediction method of DGPMIF based on multi-information fusion
In order to solve the problem of being unable to reveal complex biological processes and disease mechanisms using only a single biological data,proposed a disease-causing gene prediction method,DGPMIF,adopting a multi-information fusion strategy.Firstly,a heterogeneous network with disease-phenotype,disease-gene,protein-protein and gene-ontology associations was constructed.The network embedding algorithm was used to extract the low-dimensional vector representation of the nodes in the heterogeneous network.At the same time,the network topology algorithm was combined to extract network structural characteristics.Secondly,the cosine similarity algorithm was used to measure the similarity of node vectors and predict the relationship between diseases and genes.Finally,the effectiveness of the DGPMIF method was verified through case studies of specific diseases and comparison with classic disease-causing gene prediction methods.The results show that different types of associated data play an important role in enhancing the prediction performance of disease-causing genes,and the predictive performance of disease-causing gene prediction is improved through multi-level information fusion.DGPMIF prediction method can efficiently mine the information contained in the network,and has important reference value for prediction research on gene association of related diseases.
other disciplines of artificial intelligencedisease-causing genesheterogeneous networkinformation fusionnetwork embeddingnetwork structural characteristics
万方数据
维普
