title: Determining minimum set of driver nodes in protein-protein interaction networks.
abstract: BACKGROUND
  Recently, several studies have drawn attention to the determination of a minimum set of driver proteins that are important for the control of the underlying protein-protein interaction (PPI) networks. In general, the minimum dominating set (MDS) model is widely adopted. However, because the MDS model does not generate a unique MDS configuration, multiple different MDSs would be generated when using different optimization algorithms. Therefore, among these MDSs, it is difficult to find out the one that represents the true driver set of proteins.
  
  
  RESULTS
  To address this problem, we develop a centrality-corrected minimum dominating set (CC-MDS) model which includes heterogeneity in degree and betweenness centralities of proteins. Both the MDS model and the CC-MDS model are applied on three human PPI networks. Unlike the MDS model, the CC-MDS model generates almost the same sets of driver proteins when we implement it using different optimization algorithms. The CC-MDS model targets more high-degree and high-betweenness proteins than the uncorrected counterpart. The more central position allows CC-MDS proteins to be more important in maintaining the overall network connectivity than MDS proteins. To indicate the functional significance, we find that CC-MDS proteins are involved in, on average, more protein complexes and GO annotations than MDS proteins. We also find that more essential genes, aging genes, disease-associated genes and virus-targeted genes appear in CC-MDS proteins than in MDS proteins. As for the involvement in regulatory functions, the sets of CC-MDS proteins show much stronger enrichment of transcription factors and protein kinases. The results about topological and functional significance demonstrate that the CC-MDS model can capture more driver proteins than the MDS model.
  
  
  CONCLUSIONS
  Based on the results obtained, the CC-MDS model presents to be a powerful tool for the determination of driver proteins that can control the underlying PPI networks. The software described in this paper and the datasets used are available at https://github.com/Zhangxf-ccnu/CC-MDS .
authors.affiliation: School of Mathematics and Statistics, Central China Normal University, Luoyu Road, Wuhan, 430079, China. zhangxf@mail.ccnu.edu.cn.
authors.forename: Xiao-Fei
authors.initials: XF
authors.lastname: Zhang
authors.affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. ouyangle@mail2.sysu.edu.cn.
authors.forename: Le
authors.initials: L
authors.lastname: Ou-Yang
authors.affiliation: School of Mathematics and Statistics, Guangdong University of Finance and Economics, ChiSha Road, Guangzhou, 510320, China. zhuyuan7@mail2.sysu.edu.cn.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China. wu.mengyun@mail.shufe.edu.cn.
authors.forename: Meng-Yun
authors.initials: MY
authors.lastname: Wu
authors.affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.
authors.forename: Dao-Qing
authors.initials: DQ
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D006801:Humans; D008962:Models, Theoretical; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D012984:Software
chemical_list: D011506:Proteins