Edge biomarkers for classification and prediction of phenotypes

Zeng, Tao; Zhang, WanWei; Yu, Xiangtian; Liu, Xiaoping; Li, MeiYi; Li, Rui; Chen, Luonan

doi:10.1007/s11427-014-4757-4

Cited by 33 publications

(23 citation statements)

References 64 publications

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“…This means that the underlying GRN is quantitatively assessed via its statistical classification or, in general, prediction abilities that can be brought into contact with clinical outcome variables, e.g., disease grade, survival time, or therapeutic response. Of course, this is not limited to GRNs, but also holds for other network types that could serve as structural biomarkers, e.g., [10,[57][58][59][60][61].…”

Section: Clinical Validation Of Grnsmentioning

confidence: 99%

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Emmert‐Streib

Dehmer

2018

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Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

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Section: Clinical Validation Of Grnsmentioning

confidence: 99%

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Emmert‐Streib

Dehmer

2018

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“…Rapid progress at the front of omic technology enables discovery of novel disease biomarkers reflecting changes of an entire metabolic network (termed network biomarkers) Zhang et al, 2015). Recently, a novel model-free method based on nonlinear dynamic theory, termed dynamical network biomarkers (DNB), was developed to characterize critical transition (or early-warning signals) during the progression of complex diseases (Chen et al, 2012;Liu et al, 2012Liu et al, , 2013Liu et al, , 2014Zeng et al, 2014) in a completely different way from traditional biomarkers. DNB is a group of molecules with strong collective fluctuations, appearing only at the 'tipping point' of a homeostatic system.…”

Section: Introductionmentioning

confidence: 99%

Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers

Sa¹,

Zhang²,

Ge³

et al. 2016

Journal of Molecular Cell Biology

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Nonalcoholic fatty liver disease (NAFLD) is a major risk factor for type 2 diabetes and metabolic syndrome. However, accurately differentiating nonalcoholic steatohepatitis (NASH) from hepatosteatosis remains a clinical challenge. We identified a critical transition stage (termed pre-NASH) during the progression from hepatosteatosis to NASH in a mouse model of high fat-induced NAFLD, using lipidomics and a mathematical model termed dynamic network biomarkers (DNB). Different from the conventional biomarker approach based on the abundance of molecular expressions, the DNB model exploits collective fluctuations and correlations of different metabolites at a network level. We found that the correlations between the blood and liver lipid species drastically decreased after the transition from steatosis to NASH, which may account for the current difficulty in differentiating NASH from steatosis based on blood lipids. Furthermore, most DNB members in the blood circulation, especially for triacylglycerol (TAG), are also identified in the liver during the disease progression, suggesting a potential clinical application of DNB to diagnose NASH based on blood lipids. We further identified metabolic pathways responsible for this transition. Our study suggests that the transition from steatosis to NASH is not smooth and the existence of pre-NASH may be partially responsible for the current clinical limitations to diagnose NASH. If validated in humans, our study will open a new avenue to reliably diagnose pre-NASH and achieve early intervention of NAFLD.

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“…exploring network information for biomarker discovery. On the other hand, based on the biological data, many interactions or associations among molecules can be embedded into the biomarker discovery or directly used as novel biomarkers for disease prediction [32,34]. Biomarkers are evolving from individual molecules to a network of molecules ( Figure 1), e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Biomarkers are evolving from individual molecules to a network of molecules ( Figure 1), e.g. network biomarkers or edge biomarkers [21,32,34]. It has been well recognized that a biological function or signal transduction involved in phenotype changes, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…It has been well recognized that a biological function or signal transduction involved in phenotype changes, e.g. disease occurrence or disease recovery [32,34], is facilitated by the associated interactions (or edges) between molecules, rather than individual molecules. In fact, the concept of 'edgotype' has already linked the genotype as interactions to phenotype [35].…”

Section: Introductionmentioning

confidence: 99%

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Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals

Zeng¹,

Zhang²,

Yu³

et al. 2015

Brief Bioinform

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Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically 'small-sample size in high-dimension space', i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as 'large-sample size in low-dimension space', i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression data from a malaria vaccine trial by big-data-based edge biomarkers from module network rewiring-analysis. The illustrative results show that the identified module biomarkers can accurately distinguish vaccines with or without protection and outperformed previous reported gene signatures in terms of effectiveness and efficiency.

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Edge biomarkers for classification and prediction of phenotypes

Cited by 33 publications

References 64 publications

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers

Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals

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