Computer Science ›› 2022, Vol. 49 ›› Issue (1): 264-270.doi: 10.11896/jsjkx.201100129

• Artificial Intelligence • Previous Articles     Next Articles

Disease Genes Recognition Based on Information Propagation

LI Jia-wen, GUO Bing-hui, YANG Xiao-bo, ZHENG Zhi-ming   

  1. Beijing Advanced Innovation Center for Big Data and Brain Computing,Beihang University,Beijing 100191,China
    Peng Cheng Laboratory,Shenzhen,Guangdong 518055,China
    Key Laboratory of Mathematics,Informatics and Behavioral Semantics,School of Mathematical Sciences,Beihang University,Beijing 100191, China
  • Received:2020-11-17 Revised:2021-04-18 Online:2022-01-15 Published:2022-01-18
  • About author:LI Jia-wen,born in 1996,postgraduate,is a member of China Computer Federation.His main research interests include complex networks and bioinformatics.
    GUO Bing-hui,born in 1982,associate professor,is a professional member of China Computer Federation.His main research interests include data science and complex intelligent system.
  • Supported by:
    Artificial Intelligence Project(2018AAA0102301),National Natural Science Foundation of China(11671025) and Fundamental Research of Civil Aircraft(MJ-F-2012-04).

Abstract: 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.

Key words: Bioinformatics, Complex networks, Gene function prediction, Information propagation

CLC Number: 

  • R319
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