Journal of Theoretical Biology 362 (2014) 1–2

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Editorial

Network-based biomarkers for complex diseases

Biomarkers are important for accurate disease diagnosis and further for timely intervention. In the post-genomic era, it is becoming recognized that most diseases are resulting from the dysregulation of multiple correlated genes instead of single genes. Therefore, more and more attentions are being paid to the study of complex diseases (e.g., cancers and diabetes) from systematics perspective rather than functions of individual molecules. In particular, the biological networks that can describe the biological systems in an accurate way are widely explored to detect biomarkers. In this special issue, we report the recent progress on computational approaches that are developed to identify biomarkers for complex diseases based on biological networks. Lu et al. (2014) presented a new approach to identify co-expression modules as biomarkers that are able to discriminate the cancers from normal samples, where each module consists of multiple functional related genes. The results on several cancer datasets demonstrate the effectiveness of the module biomarkers for cancer diagnosis. Inflammation plays important role in the success of kidney transplantation. By mapping inflammation proteins to a human protein–protein interaction network, Wu et al. (2014) identified some important proteins that are related to the transmission of distinct phenotypes of kidney transplantation. These proteins can not only separate different phenotypes but also help explain how different phenotypes changes. In general, most works for biomarker detection focus on nodes or molecules in the biological network, i.e. genes/proteins, with the assumption that those differentially expressed genes are related to diseases. However, there are also many genes, whose expressions do not change significantly but the regulations between these genes are rewired or have differential correlations due to the diseases. For example, there are strong regulations in normal samples but those regulations disappear in disease samples. Zhang et al. (2014) proposed a novel edge biomarker concept to identify regulations/interactions between genes that are related to diseases. Different from traditional networkbased biomarkers, the edge biomarkers directly use network information or correlation-like information for identifying the disease state on each sample. The results on both human cholangiocarcinoma and diabetes datasets indicate that the edge biomarkers perform better than node biomarkers. Wong et al. (2014) developed a novel approach to identify core and specific network-based markers for distinct cancer types by constructing cancer-specific networks. These network based biomarkers provide new insights into the common and specific pathways underlying carcinogenesis. Instead of genes, the molecular pathways can better explain how the biological systems work and behave. Lu (2014) proposed a new pathway-based feature selection method to identify genes that are able to discriminate distinct http://dx.doi.org/10.1016/j.jtbi.2014.07.007 0022-5193/& 2014 Elsevier Ltd. All rights reserved.

subtypes of sarcomas, and obtained promising results on real datasets. Qin and Zhao (2014) made a survey on recent progress of computational approaches for identification of disease biomarkers based on molecular networks, which can help biomedical scientists to choose appropriate methods or tools for their questions of interest. Regenerative medicine is always a hot topic in the biomedicine field. However, how cells maintain their replicative capacity is still unclear. Hu et al. (2014) investigated the diauxic growth of Saccharomyces cerevisiae based on a dynamical system describing the behavior of the molecular–cellular network of S. cerevisiae. Wu et al. (2014) reviewed the common approaches and tools that can be used to analyze biological networks, especially those that can be used for proteomics data together with networks. Despite the good performance of network biomarkers, our current knowledge about biological networks, e.g., network analysis and network reconstruction, is far from complete. Wang (2014) discussed the statistical methods used to reconstruct gene regulatory networks based on gene expression data, which can be utilized for future biomarker identification. In this special issue, we mainly stress on one aspect of biological systems for biomedical application, i.e., network, and as another aspect of biological systems, dynamics (Liu et al., 2014) should also be considered along with the network in systems biology study, in particular for the early diagnosis by detecting dynamical network biomarkers (Chen et al., 2012). As guest editors of this special issue, we wish to thank all authors of the papers published in this special issue and all referees for their insightful and constructive comments that have significantly improved the quality of the issue. Specifically, we want to thank the editor (Sun Kim) and Chief Editors for their help and support of this issue. Finally, we would also like to thank Janet Stein for her kind assistance in preparing this special issue.

References Chen, L., Liu, R., Liu, Z.P., Li, M., Aihara, K., 2012. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci. Rep. 2, 342. Gu, J.L., Lu, Y., Liu, C., Lu, H., 2014. Multiclass classification of sarcomas using pathway based feature selection method. J. Theor. Biol. pii,, S0022-5193(14) 00381-6. Hu, J., Zhu, X., Wang, X., Yuan, R., Zheng, W., Xu, M., Ao, P., 2014. Two programmed replicative lifespans of Saccharomyces cerevisiae formed by the endogenous molecular–cellular network. J. Theor. Biol. pii,, S0022-5193(14)00016-2. Liu, R., Wang, X., Aihara, K., Chen, L., 2014. Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers. Med. Res. Rev. 34, 455–478. Lu, X., Deng, Y., Huang, L., Feng, B., Liao, B., 2014. A co-expression modules based gene selection for cancer recognition. J. Theor. Biol. pii,, S0022-5193 (14)00014-9.

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Editorial / Journal of Theoretical Biology 362 (2014) 1–2

Qin, G., Zhao, X.M., 2014. A survey on computational approaches to identifying disease biomarkers based on molecular networks. J. Theor. Biol. pii,, S00225193(14)00344-0. Wang, Y.X., Huang, H., 2014. Review on statistical methods for gene network reconstruction using expression data. J. Theor. Biol. pii,, S0022-5193(14)00196-9. Wong, Y.H., Chen, R.H., Chen, B.S., 2014. Core and specific network markers of carcinogenesis from multiple cancer samples. J. Theor. Biol. pii,, S0022-5193(14) 00334-8. Wu, D., Liu, X., Liu, C., Liu, Z., Xu, M., Rong, R., Qian, M., Chen, L., Zhu, T., 2014. Network analysis reveals roles of inflammatory factors in different phenotypes of kidney transplant patients. J. Theor. Biol. pii,, S0022-5193(14)00141-6. Wu, X., Hasan, M.A., Chen, J.Y., 2014. Pathway and network analysis in proteomics. J. Theor. Biol. pii,, S0022-5193(14)00304-X. Zhang, W., Zeng, T., Chen, L., 2014. EdgeMarker: identifying differentially correlated molecule pairs as edge-biomarkers. J. Theor. Biol. pii,, S0022-5193(14)00324-5.

Xing-Ming Zhao n School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China E-mail address: [email protected] Luonan Chen Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Available online 10 July 2014

n

Corresponding author.

Network-based biomarkers for complex diseases.

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