Differential dependency network analysis to identify condition-specific topological changes in biological networks

Zhang, Bai; Li, Huai; Riggins, Rebecca B.; Zhan, Ming; Xuan, Jianhua; Zhang, Zhen; Hoffman, Eric P.; Clarke, Robert; Wang, Yue

doi:10.1093/bioinformatics/btn660

Cited by 107 publications

(108 citation statements)

References 54 publications

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“…Differential gene expression Differential gene co-expression Scale of differential network GNEA [60] Yes A set of interactive genes DEGAS [61] Yes Dys-regulated genes enriched on sub-networks DDN [62,63] Yes Local dependency network PNA [64] Yes Yes Major dynamic activation patterns within sub-networks DNE [65] Yes Yes Local change of differential network entropy DEN [66] Yes Yes Global differential expression network PMN [67] Yes Yes Network modules, module network and its re-organizations…”

Section: Methodsmentioning

confidence: 99%

“…Differential dependency network (DDN) analysis exploits the differential topological changes in biological networks. Based on a local dependency model, DDN detects significant topological changes in the transcriptional networks between two biological conditions, rather than changes in their expression levels [62,63]. By contrast, principal network analysis (PNA) captures the major dynamic activation patterns and their associated protein and metabolic sub-networks under multiple conditions [64].…”

Section: Appendixmentioning

confidence: 99%

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Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

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Section: Methodsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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show abstract

“…A vivid example is the gene regulatory networks, which, under different conditions, exhibit different regulation patterns accompanied by different transcriptional network topologies (cf. Zhang et al [16] and Luscombe et al [13]). As one of the most investigated network-specific dynamic, network synchronizability shows to vary as the network structure varies.…”

Section: Introductionmentioning

confidence: 98%

Synchrony and Elementary Operations on Coupled Cell Networks

Aguiar¹,

Dias²,

Ruan³

2016

SIAM J. Appl. Dyn. Syst.

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Given a finite graph (network), let every node (cell) represent an individual dynamics given by a system of ordinary differential equations, and every arrow (edge) encode the dynamical influence of the tail node on the head node. We then have defined a coupled cell system that is associated with the given network structure. Subspaces that are defined by equalities of cell coordinates and left invariant under every coupled cell system respecting the network structure are called synchrony subspaces. They are completely determined by the network structure and form a complete lattice under set-inclusions. We analyze the transition of the lattice of synchrony subspaces of a network that is caused by structural changes in the network topology, such as deletion and addition of cells or edges, and rewirings of edges. We give necessary, and in some cases sufficient, conditions under which lattice elements persist or disappear.AMS classification scheme numbers: 34C15 37C10 05C76 06B23

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“…In several real world data, such as that from the stock market (Baillie and Bollerslev, 1989), gene regulatory networks (Ahmed and Xing, 2009;Zhang et al, 2009), biomedical measurements (Varoquaux et al, 2010), or sensors in engineering systems (Idé et al, 2009), there are dynamical properties over time evolutions or due to changes in the surrounding environments. Such effects cause data to have different behaviors in each dataset collected under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Learning a common substructure of multiple graphical Gaussian models

Hara

Washio

2013

Neural Networks

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Properties of data are frequently seen to vary depending on the sampled situations, which usually changes along a time evolution or owing to environmental effects. One way to analyze such data is to find invariances, or representative features kept constant over changes. The aim of this paper is to identify one such feature, namely interactions or dependencies among variables that are common across multiple datasets collected under different conditions. To that end, we propose a common substructure learning (CSSL) framework based on a graphical Gaussian model. We further present a simple learning algorithm based on the Dual Augmented Lagrangian and the Alternating Direction Method of Multipliers. We confirm the performance of CSSL over other existing techniques in finding unchanging dependency structures in multiple datasets through numerical simulations on synthetic data and through a real world application to anomaly detection in automobile sensors.

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Differential dependency network analysis to identify condition-specific topological changes in biological networks

Abstract: The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.

Cited by 107 publications

References 54 publications

Edge biomarkers for classification and prediction of phenotypes

Edge biomarkers for classification and prediction of phenotypes

Synchrony and Elementary Operations on Coupled Cell Networks

Learning a common substructure of multiple graphical Gaussian models

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