Algorithms for effective querying of compound graph-based pathway databases

Doğrusöz, Uğur; Çetintaş, Ahmet; Demir, Emek; Babur, Özgün

doi:10.1186/1471-2105-10-376

Cited by 28 publications

(43 citation statements)

References 29 publications

(42 reference statements)

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“…Similarly, undirected hypergraphs allow interactions among two or more entities [10]. Software that computes paths, loops, and motifs on compound graphs [8] and visualizes metagraphs [15] have accelerated the adoption of these representations.…”

Section: Related Researchmentioning

confidence: 99%

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Pathway analysis with signaling hypergraphs

Ritz

Murali

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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Signaling pathways play an important role in the cell's response to its environment. Signaling pathways are often represented as directed graphs, which are not adequate for modeling reactions such as complex assembly and dissociation, combinatorial regulation, and protein activation/inactivation. More accurate representations such as directed hypergraphs remain underutilized. In this paper, we present an extension of a directed hypergraph that we call a signaling hypergraph. We formulate a problem that asks what proteins and interactions must be involved in order to stimulate a specific response downstream of a signaling pathway. We relate this problem to computing the shortest acyclic B-hyperpath in a signaling hypergraph -an NP-hard problem -and present a mixed integer linear program to solve it. We demonstrate that the shortest hyperpaths computed in signaling hypergraphs are far more informative than shortest paths found in corresponding graph representations. Our results illustrate the potential of signaling hypergraphs as an improved representation of signaling pathways and motivate the development of novel hypergraph algorithms.

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Section: Related Researchmentioning

confidence: 99%

“…Hypernodes may be referred to as undirected hyperedges, compound nodes[9,8], or metanodes[15] in the literature.…”

mentioning

confidence: 99%

Pathway analysis with signaling hypergraphs

Ritz

Murali

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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“…Efforts like FAIRDOMhub, the NDex platform, and the Physiome Model Repository go in a similar direction [23][24][25]. The effective use of a shared repository is only possible with a powerful set of queries including graph-based ones such as shortest paths between a specified set of molecules and common target of a gene set [26]. Here a milestone will be a translation of one or more common modules between different disease maps.…”

Section: Integrating Maps In a Shared Repositorymentioning

confidence: 99%

Community-driven roadmap for integrated disease maps

Ostaszewski¹,

Gebel²,

Kuperstein³

et al. 2018

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The Disease Maps Project builds upon a network of scientific and clinical groups that exchange best practices, share information, and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation, or serve as a scaffold for the generation of predictive mathematical models.This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and web-tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

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“…Proteomic data has been used in the development of various software utilities used in the analyses of genomic and proteomic data. PPI data from HPRD has been used in several interactomebased biological network visualization tools such as DASMIweb [51,52], DynaMod [53], PATIKAweb [54], POINeT [55], SubExtractor [56], NASCENT [57], ConsensusPathDB [58], VisANT [59], Genes2Networks [60], Cerebral [61,62], BioNetBuilder [62], COXPRESdb [63], STRING 8 [64], and UniHI [65]. Proteomic information from HPRD has been utilized in the functional analysis of an integrin adhesome network [66].…”

Section: Human Protein Reference Database As a Platform For Systems Bmentioning

confidence: 99%

Human Protein Reference Database and Human Proteinpedia as Discovery Resources for Molecular Biotechnology

et al. 2010

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In the recent years, research in molecular biotechnology has transformed from being small scale studies targeted at a single or a small set of molecule(s) into a combination of high throughput discovery platforms and extensive validations. Such a discovery platform provided an unbiased approach which resulted in the identification of several novel genetic and protein biomarkers. High throughput nature of these investigations coupled with higher sensitivity and specificity of Next Generation technologies provided qualitatively and quantitatively richer biological data. These developments have also revolutionized biological research and speed of data generation. However, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Data resources became necessary to assimilate, store and disseminate information that could allow future discoveries. We have developed two resources--Human Protein Reference Database (HPRD) and Human Proteinpedia, which integrate knowledge relevant to human proteins. A number of protein features including protein-protein interactions, post-translational modifications, subcellular localization, and tissue expression, which have been studied using different strategies were incorporated in these databases. Human Proteinpedia also provides a portal for community participation to annotate and share proteomic data and uses HPRD as the scaffold for data processing. Proteomic investigators can even share unpublished data in Human Proteinpedia, which provides a meaningful platform for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, molecular biology, cloning, and classical genetics in understanding the relationships among multiple facets of biological systems.

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Algorithms for effective querying of compound graph-based pathway databases

Cited by 28 publications

References 29 publications

Pathway analysis with signaling hypergraphs

Pathway analysis with signaling hypergraphs

Community-driven roadmap for integrated disease maps

Human Protein Reference Database and Human Proteinpedia as Discovery Resources for Molecular Biotechnology

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