title: Comparative network stratification analysis for identifying functional interpretable network biomarkers.
abstract: BACKGROUND
  A major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions.
  
  
  RESULTS
  In this paper, we present a new computational framework based on an integer programming model, named as Comparative Network Stratification (CNS), to extract functional or interpretable network biomarkers, which are of strongly discriminative power on disease states and also readable interpretation on biological functions. In addition, CNS can not only recognize the pathogen biological functions disregarded by traditional Expression-based/Network-based methods, but also uncover the active network-structures underlying such dysregulated functions underestimated by traditional Function-based methods. To validate the effectiveness, we have compared CNS with five state-of-the-art methods, i.e. GSVA, Pathifier, stSVM, frSVM and AEP on four datasets of different complex diseases. The results show that CNS can enhance the discriminative power of network biomarkers, and further provide biologically interpretable information or disease pathogenic mechanism of these biomarkers. A case study on type 1 diabetes (T1D) demonstrates that CNS can identify many dysfunctional genes and networks previously disregarded by conventional approaches.
  
  
  CONCLUSIONS
  Therefore, CNS is actually a powerful bioinformatics tool, which can identify functional or interpretable network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. CNS was implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/CNSpackage_0.1.rar .
authors.affiliation: State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China.|Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
authors.forename: Chuanchao
authors.initials: C
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China. liujuan@whu.edu.cn.
authors.forename: Juan
authors.initials: J
authors.lastname: Liu
authors.affiliation: Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
authors.forename: Qianqian
authors.initials: Q
authors.lastname: Shi
authors.affiliation: Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. zengtao@sibs.ac.cn.
authors.forename: Tao
authors.initials: T
authors.lastname: Zeng
authors.affiliation: Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. lnchen@sibs.ac.cn.
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
mesh_terms: D015415:Biomarkers / Q000097:blood; D019295:Computational Biology; D030541:Databases, Genetic; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D006801:Humans; D007328:Insulin / Q000097:blood / Q000378:metabolism; D000078790:Insulin Secretion; D008962:Models, Theoretical; D057285:Precision Medicine
chemical_list: D015415:Biomarkers; D007328:Insulin
keywords: Complex disease; Integer programming; Network biomarker; Network stratification