SPIKE: a database of highly curated human signaling pathways

Paz, Arnon; Brownstein, Zippora; Ber, Yaara; Bialik, Shani; David, E MacHugh; Sagir, Dorit; Ulitsky, Igor; Elkon, Ran; Kimchi, Adi; Avraham, Karen B.; Shiloh, Yosef; Shamir, Ron

doi:10.1093/nar/gkq1167

Cited by 79 publications

(57 citation statements)

References 20 publications

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“…We will start this section on a brief discussion on some of the most prominent databases, such as KEGG [67], Reactome [26], and SPIKE [94], which store signaling pathways in the form of hyper- [27,60,67].…”

Section: Motivation Behind Inferring Hypergraphsmentioning

confidence: 99%

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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Section: Motivation Behind Inferring Hypergraphsmentioning

confidence: 99%

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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“…The step forward is to use molecular information detailed in pathways and signaling network resources as Panther [23], Spike [24], Kegg Pathway [25], Reactome [26], ACSN [27] (Table 1). These resources provide a more global picture of cell signaling with sufficient granularity of molecular detail description, capturing crosstalks and feedback loops between molecular circuits.…”

Section: Signaling Network and High-throughput Data For Deciphering Amentioning

confidence: 99%

Network-based approaches for drug response prediction and targeted therapy development in cancer

Dorel

Barillot

Zinovyev

et al. 2015

Biochemical and Biophysical Research Communications

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“…In this study, six BioPAX-modelled pathway databases, including PID (contains only the data curated by PID, version 2012.03.17), BioCarta (from PID, version 2010.08.11), Reactome (version 2012.03.14) [18], NetPath (downloaded 2012.04.14) [19], INOH (version 2011.01.31) [20] and SPIKE (version 2011.03.22) [21], were utilized. The non-metabolic pathway portion of KEGG (downloaded 2009.10.12) [22] was also included because of its large data size.…”

Section: Pathway and Ppi Databasesmentioning

confidence: 99%

PathPPI: an integrated dataset of human pathways and protein-protein interactions

Tang

Zhong

Liu

et al. 2015

Sci. China Life Sci.

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Integration of pathway and protein-protein interaction (PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, BioCarta, Reactome, NetPath, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, IntAct, BioGRID, MINT and DIP, to develop integrated dataset (named PathPPI). More detailed interaction type and modification information on protein interactions can be preserved in PathPPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values (O AB ) among these pathway databases were less than 5%, and these databases must be used conjunctively. The PathPPI data was provided at

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SPIKE: a database of highly curated human signaling pathways

Cited by 79 publications

References 20 publications

Unstable Communities in Network Ensembles

Unstable Communities in Network Ensembles

Network-based approaches for drug response prediction and targeted therapy development in cancer

PathPPI: an integrated dataset of human pathways and protein-protein interactions

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