title: An ensemble framework for identifying essential proteins.
abstract: BACKGROUND
  Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small.
  
  
  RESULTS
  In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins.
  
  
  CONCLUSIONS
  This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.
authors.affiliation: Systems Biology Core, NHLBI, NIH, 9000 Rockville Pike, Bethesda, MD, 20892, USA.
authors.forename: Xue
authors.identifier: http://orcid.org/0000-0002-9120-0459
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, XiangNan University, Eastern Wangxian Park, Chenzhou, Hunan, 423000, China. xiaozhangdoctor@126.com.
authors.forename: Wangxin
authors.initials: W
authors.lastname: Xiao
authors.affiliation: Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP-São Paulo State University, CEP 18618-970, Botucatu, São Paulo, 510, Brazil.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), P.B. 8905, N-7491, Trondheim, Norway.
authors.forename: Marcio Luis
authors.initials: ML
authors.lastname: Acencio
authors.affiliation: Department of Physics and Biophysics, Institute of Biosciences of Botucatu, UNESP-São Paulo State University, CEP 18618-970, Botucatu, São Paulo, 510, Brazil.
authors.forename: Ney
authors.initials: N
authors.lastname: Lemke
authors.affiliation: Systems Biology Core, NHLBI, NIH, 9000 Rockville Pike, Bethesda, MD, 20892, USA. xujing.wang@nih.gov.
authors.forename: Xujing
authors.initials: X
authors.lastname: Wang
mesh_terms: D015870:Gene Expression; D016681:Genome, Fungal; D016366:Open Reading Frames; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins
keywords: Centrality measure; Ensemble learning; Essential protein; Gene expression; Protein-protein interaction networks