基于信息传播的致病基因识别研究

Genetic research in the field of life science and medicine occupies an important position, while disease genes are one of its key focuses. Accurate identification of disease-causing genes can reveal the pathogenesis of diseases at the molecular level, and provide strong support for the prevention, diagnosis, treatment and other medical stages of diseases. The key to accurately identifying disease-causing genes is to give a measure of similarity between genes. This paper uses complex networks to model biological systems and proposes a dissipative random walk model with multiple restarts to measure the degree of functional similarity between genes. Firstly, a human gene-gene interaction network is constructed based on the human gene interaction datasets on NCBI. Experiments are then carried out on KEGG's disease-gene association dataset to identify known disease-causing genes. Compared with the three existing models of SP, RWR and PRINCE, DRWMR accurately predicts 156 of 581 diseases while the remaining models predict 121. 3 correctly on average. The average prediction score of DRWMR is 9. 46% higher. Finally, the potential disease genes of asthma, hemophilia and PEHO syndrome are predicted and the candidate genes are found guilty for the pathologies in the literature or biological database.