Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221200113-8.doi: 10.11896/jsjkx.221200113

• Artificial Intelligence • Previous Articles     Next Articles

Method for Identifying Active Module Based on Gene Prioritization

ZHANG Qi1, PAN Ke2, ZHU Kai3   

  1. 1 Yimeng Executive Leadership Academy,Linyi,Shandong 276000,China
    2 College of Artificial Intelligence and Software,Nanning College,Nanning 530000,China
    3 School of Computer Science,Wuhan University,Wuhan 430000,China
  • Published:2023-11-09
  • About author:ZHANG Qi,born in 1995,master.Her main research interests include bioinformatics and algorithm optimization.

Abstract: With the rapid development of high-throughput sequencing,a vast amount of multi-omics data has been contributed to investigating the pathogenesis of cancer at the molecular level.In recent years,the identification of active modules has become a major direction in bioinformatics.However,many existing approaches cannot identify a dense module that has strong association with cancer.A method called IdeMod is proposed by integrating protein-protein interaction network(PPI) and gene expression data.More concretely,a gene scoring function is devised by using the regression model with a p-step random walk kernel.By introducing the relationship of dominance in the POC method,a gene prioritization list is presented.A simulated annealing algorithm SA-PROX is introduced to find an active module with high gene prioritization and strong connectivity.Experiments are performed on real biological datasets,including breast cancer and cervical cancer.Compared with the previous methods SigMod,LEAN,RegMod and ModFinder,IdeMod can successfully identify a well-connected module that contains a large proportion of cancer-related genes.Therefore,the proposed approach may become a useful complementary tool for identifying active module.

Key words: Cancer, Active module, Gene prioritization, Protein-protein interaction network, Gene expression, Simulated annealing algorithm

CLC Number: 

  • TP301
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