Towards zoomable multidimensional maps of the cell

Hu, Zhenjun; Mellor, Joe; Wu, Jie; Kanehisa, Minoru; Stuart, Joshua M.; DeLisi, Charles

doi:10.1038/nbt1304

Cited by 81 publications

(84 citation statements)

References 59 publications

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“…It has been suggested that modules in biological networks follow a hierarchical organizational scheme [13], [21]. However, most modularization algorithms, including dMoNet, are concerned primarily with identifying functional modules that lie at a low level in this hierarchy for the purpose of generating immediately useful and more easily testable hypotheses.…”

Section: F Framing the Comparison To MCL Resultsmentioning

confidence: 99%

Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks

Chang¹,

Luo²,

Johnson³

et al. 2010

IJBRA

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Abstract-Accumulating evidence suggests that biological systems exhibit modular organization. Accurate identification of modularity is vital for understanding this organization. A recent approach, Modules of Networks (MoNet), introduced an intuitive module definition and a clear detection method based on a ranked list of edges generated by the Girvan-Newman (G-N) algorithm. The resulting modules from a yeast network showed significant association with known biological processes, indicative of the method's utility. Despite MoNet's usefulness, systematic bias in the method leads to varied results across trials. MoNet modules also exclude some network regions. Such deficiencies limit meaningful analysis of a network. To address these shortcomings, we have developed a deterministic G-N algorithm (dG-N) and a new agglomerative algorithm, Deterministic Modularization of Networks (dMoNet). dMoNet simultaneously processes structurally equivalent edges while preserving the intuitive foundations of the MoNet algorithm. dMoNet also includes all network nodes in the identified modules, thus generating hypotheses with respect to a full network. A GO-based method for comparison of dMoNet to other modularization methods, including a currently favored algorithm, shows an overall better performance by dMoNet when analyzing large-scale yeast and human protein interaction networks. This comparison method comprises a rational evaluation of the quality of functional modules identified from large-scale interaction networks. The code and resulting data from this work are available upon request.

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Section: F Framing the Comparison To MCL Resultsmentioning

confidence: 99%

Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks

Chang¹,

Luo²,

Johnson³

et al. 2010

IJBRA

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show abstract

“…A compound node either represents a single entity (e.g., a protein Compound graphs have been used to represent simple signaling pathways [41]. They are also useful to represent the compositions of protein complexes and the connections between their components (if any) [58]. However, they have a number of disadvantages, as follows.…”

Section: Compound Graphsmentioning

confidence: 99%

“…A child node can be either a simple node or a metanode. The edges in E describe different types of relationships between two nodes in V , for e.g., physical interaction, reaction, or sharing of member nodes [58]. Thus, a metagraph can be considered as an extension of a compound graph which does not enforce the 'decomposition tree condition' upon its nodes.…”

Section: Metagraphsmentioning

confidence: 99%

“…(i) Compound graphs are unable to represent multiple compound nodes that share the same compound node(s) [58] because, in such a case, the set of decomposition edges (F ), will not form a rooted tree, which is a requirement of compound graphs. For instance, consider the reactions in ).…”

Section: Compound Graphsmentioning

confidence: 99%

“…An edge in a graph can only represent a pairwise relationship; it cannot sufficiently represent an interaction among three or more molecules (henceforth called multi-way interactions), such as a protein complex that acts as a unit ( [11,24,52,53,58,71,138].…”

Section: Limitations Of Graphsmentioning

confidence: 99%

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Unstable Communities in Network Ensembles

Rahman

Jan

Kim³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Ensembles of graphs arise naturally in many applications, for example, the temporal evolution of social contacts or computer communications, tissue-specific protein interaction networks, annual citation or co-authorship networks in a field, or a family of high-likelihood Bayesian networks inferred from systems biology data. Several techniques have been developed to analyze such ensembles. A canonical problem is that of computing communities that are persistent across the ensemble. This problem is usually formulated as one of computing dense subgraphs (communities) that are frequent, i.e., appear in many graphs in the ensemble.In this thesis, we seek to find "unstable communities," which are the antithesis of frequent, dense subgraphs. Informally, an unstable community is a set of nodes that induces highly-varying subgraphs in the ensemble. In other words, the graphs in the ensemble disagree about the precise pairwise connections among these nodes. The primary contribution of this dissertation is to introduce the concept of unstable communities as a novel problem in the field of graph mining. Specifically, it presents three approaches to mathematically formulate the concept of unstable communities, devises algorithms for computing such communities in a given ensemble of networks, and shows the usefulness of this concept in a variety of settings.Our first definition of unstable community relies on two parameters: the first ensures that a node set induces several different subgraphs in the ensemble and the second guarantees that each of these subgraphs occurs in a large number of graphs in the ensemble. We present two algorithms to enumerate unstable communities that match this definition. The first approach, ClustMiner, is a heuristic that transforms the problem into one of computing dense subgraphs in a single graph that summarizes the ensemble. The second approach, UCMiner, is guaranteed to enumerate all maximal unstable communities correctly. We apply both approaches to systems biology datasets to demonstrate that UCMiner is superior to ClustMiner in the sense that ClustMiner's output contains node sets that are not unstable while also missing several communities computed by UCMiner. We find several node sets that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns.Our second and third definitions of unstable community rely on a novel concept of (scaled) subgraph divergence, a formulation that uses the concept of relative entropy to measure the instability of a community. We propose another algorithm, SDMiner, that can exactly enumerate all maximal unstable communities with small (scaled) subgraph divergence. We perform extensive experiments on social network datasets to show that we can discover UCs that capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.Dedicated to the memory of my spiritual ...

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Using VisANT to Analyze Networks

2014

CP in Bioinformatics

Self Cite

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VisANT is a Web‐based workbench for the integrative analysis of biological networks that offers unique features such as exploratory navigation of interaction network and multi‐scale visualization and inference with integrated hierarchical knowledge. It provides functionalities for convenient construction, visualization, and analysis of molecular and higher‐order networks based on functional (e.g., expression profiles, phylogenetic profiles) and physical (e.g., yeast two‐hybrid, chromatin‐immunoprecipitation, and drug target) relations from either the Predictome database or user‐defined data sets. Analysis capabilities include network structure analysis, over‐representation analysis, expression enrichment analysis, etc. Additionally, networks can be saved, accessed, and shared online. VisANT is able to develop and display meta‐networks for meta‐nodes that are structural complexes, pathways, or any kind of sub‐networks. Further, VisANT supports a growing number of standard exchange formats and database‐referencing standards, e.g., PSI‐MI, KGML, BioPAX, and SBML (in progress). Multiple species are supported to the extent that interactions or associations are available (i.e., public datasets or Predictome database). Curr. Protoc. Bioinform. 45:8.8.1‐8.8.39. © 2014 by John Wiley & Sons, Inc.

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Towards zoomable multidimensional maps of the cell

Cited by 81 publications

References 59 publications

Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks

Deterministic graph-theoretic algorithm for detecting modules in biological interaction networks

Unstable Communities in Network Ensembles

Using VisANT to Analyze Networks

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