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• W2155152257 abstract "HomeCirculation: Cardiovascular GeneticsVol. 5, No. 4Systems Proteomics for Translational Network Medicine Free AccessResearch ArticlePDF/EPUBAboutView PDFSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBSystems Proteomics for Translational Network Medicine D. Kent Arrell and Andre Terzic D. Kent ArrellD. Kent Arrell Search for more papers by this author and Andre TerzicAndre Terzic Search for more papers by this author Originally published1 Aug 2012https://doi.org/10.1161/CIRCGENETICS.110.958991Circulation: Cardiovascular Genetics. 2012;5:478Proteomic CartographyProteomics encompasses diverse methods by which to study proteins, their abundance, structure, posttranslational modifications, and physical or functional interacting partners to map the proteome, the protein complement of a genome. Although such methods can be applied to individual proteins, proteomics facilitates large-scale analysis of complete proteomes or targeted subproteomes (Table 1). Moreover, proteomics enables comparisons between distinct conditions/states, from defined biological sources, across discrete timelines. In general, a proteomic cartography pipeline involves sample acquisition, followed by protein isolation, separation, resolution, and identification (Figure). Along this continuum, each successive module harbors a multistep process, whereby proteomic methodologies can be implemented.Download figureDownload PowerPointFigure. Network systems proteomic pipeline. Strategy extending proteomic profiling from mapping and quantification of protein identities to ontological categorization, pathway analysis and network generation for inclusive systems interpretation.Table 1. Systems Proteomics LexiconSubproteomeProteome subset, corresponding to particular intra- or extracellular compartments, involvement in specific functions, or modulated by or dependent on particular biological contexts.MetaboproteomeSubproteome involved in and supporting cellular metabolic function.Systems biologyAnalytical approach to investigate and model relationships among system components to understand and predict emergent properties.Network/interactomeMathematical representation of pairwise collections of proteins connected by interdependent interactions and relationships, the structure of which conveys emergent properties.Network medicine/biologyAnalysis of molecular (eg, protein, gene, metabolite) interaction networks through complex network theory, comprising topological features characterizing symmetric or asymmetric relationships among discrete network objects and their known or established interactions to guide medical or biological interpretation.Node/vertexFundamental unit of a network, in network biology including one or more of the following: genes, proteins, metabolites, drugs, and endogenous small molecules.EdgeIn network biology, a relationship between 2 nodes or node self-interaction, involving physical, regulatory, or genetic interactions which may be undirected or directed (uni- or bidirectional). Edges can include information regarding relationship confidence (eg, node weighting) or function (eg, interaction/pathway kinetics).Degree distributionNode degree defines number of connections between a network node and other nodes, with degree distribution being the probability distribution of the degrees of all nodes.Clustering coefficientNode clustering coefficient is determined by proportion of nodes connected to it that also connect to one another, providing an indication of neighborhood relatedness, the degree to which nodes within a network cluster together.Scale-free topologyBiological networks are postulated to exhibit power law distributions of node connectivity, which influences emergent network properties, such as robustness.Path lengthShortest distance required to connect 2 nodes. Scale-free networks have extremely small average path lengths.Network diameterMaximum path length necessary to connect any 2 network nodes.Network motifPatterns of interconnections in complex networks occurring at significantly higher rates than expected in randomly assembled networks.Network moduleCollection of nodes with high internal connectivity but few connections with remainder of the network, typically comprising nodes sharing a particular function (eg, complex, signaling cascade).HubNode or small number of nodes much more highly connected to other network constituents than expected randomly.Bridging nodeNode bridging the shortest path between 2 other nodes or modules in a network, which can be critical bottlenecks in network functionality.Once proteins are isolated from the source of interest, 2 major separation strategies have evolved to address proteome complexity.1,2 Traditional gel-based approaches, particularly 2-dimensional gel electrophoresis involving isoelectric focusing followed orthogonally by sodium dodecyl sulfate - polyacrylamide gel electrophoresis, were the earliest methods adopted for protein separation and resolution, comparative assessment of relative abundance, and initial reduction of protein complexity before mass spectrometry (MS). Although 2-dimensional gel electrophoresis remains common, the advent of quantitative MS techniques has led to increased application of various gel-based and gel-free alternatives, whereby protein or peptide complexity is addressed by separation and resolution before or in conjunction with MS, whereas quantification is subsequently measured from MS precursor or fragment ion spectra. Choice of separation strategy is influenced by study objectives and resources, often guided by advantages and limitations to each approach. Quantitative MS methods tend to be less time- and labor-intensive while offering greater potential throughput and data acquisition, yet in instances where 2-dimensional gel electrophoresis approaches are applied in parallel, use of duplicate approaches can provide independent confirmation3,4 or yield complementary, nonoverlapping information.5,6 Regardless of separation strategy, bottom-up or top-down MS analysis7,8 facilitates protein assignment, generating extensive lists that, depending on experimental design, typically include relative abundance between conditions or states (Figure). Several reviews provide greater technical detail on proteomic methodologies and their broad-based application to cardiovascular research.1,2,9–11A decade ago, an initial survey of the cardiovascular proteomics domain emphasized that successful incorporation of existing and new technologies would help to unravel intricacies of proteome dynamics.1 Indeed, introduction of several enabling technologies has accelerated this emerging field. The utility of 2-dimensional gel electrophoresis was enhanced by improved gel reproducibility attained with immobilized pH gradient isoelectric focusing12 and fluorescent-based difference gel electrophoresis allowing coresolution of paired samples.13 Numerous MS advances included algorithms for automatic spectral interpretation and quantitative MS strategies, such as isotope-code affinity tag peptide labeling14 or stable isotope labeling of amino acids.15 Subsequent instrumentation advances, such as development of high-resolution, high-mass accuracy Fourier transform-ion cyclotron resonance MS and introduction of Orbitrap MS technology for proteomic applications,16,17 expansion of quantitative MS techniques, such as isobaric tag and label-free methods,18–20 development of selective posttranslational modification analysis strategies, eg, phosphopeptide enrichment,21 plus continued growth and extensive curation of protein databases for comparison and identification of peptides and proteins from acquired MS data, all contributed to expansion of proteomic studies.An important corollary of these developments is the increasing magnitude of acquired information. Individual proteomic data sets are not simply more numerous; they are growing ever larger. Regardless of whether gel-based or gel-free methodology is applied, if surveys are quantitative comparisons or qualitative cataloging or if they provide coverage of single or multiple time-point/phenotype dynamics, proteomic studies now regularly yield lists comprising hundreds of identified proteins. Thus, a critical consideration for continued maturation of proteomic research is defining appropriate measures by which to collate, prioritize, and integrate expansive protein lists to maximize biologically meaningful data extraction. When confronted with overwhelming protein numbers, where traditional reductionist analysis of each no longer remains feasible, it becomes tempting to focus on a particular subset, such as a fraction of proteins exhibiting greatest differential expression, those with established links to the topic of interest, or another delimiter to focus interpretation and narrow downstream analysis.22 As a consequence, mechanistically relevant proteins, complexes, and functional modules may be inadvertently marginalized or ignored. In this context, analysis of proteomic and other high-throughput data acquired using objectively defined statistical criteria23 should instead be inclusive.22Systems Algorithms for Proteome ComprehensionNetwork systems biology offers an integrative approach to achieve inclusivity and maximize comprehension. Network platforms incorporate a complement of identified proteins, together with their interactions and, where available, input of other high-throughput data, to better comprehend the system in its entirety.22,24–29 Various network analysis approaches are becoming increasingly prevalent in genomic,30–34 microRNA,35,36 proteomic,3,37–41 and metabolomic42 cardiovascular research.43–45Proteins function within intricate webs of interactions. Accordingly, derivation of biological interaction networks provides a rational means of assembling proteomic data in functional contexts. As an example, the minimal network required to incorporate all identified proteins via established interactions yields an immediate focal point of proteomic measurement within an experimental system, regardless of extent of data set size or breadth of constituent protein functional variability. Derived networks are amenable to compositional assessment, based on function/ontology overrepresentation and enrichment analysis, and to structural examination, such as architectural topology or positional relevance of components, eg, hubs or bridging nodes (Table 1).46,47 Moreover, network composition and structure may be exploited for mechanistic interpretation, hypothesis generation, and functional validation, enabling refinement of assembled networks.22,48 Network systems biology thus offers a logical, inclusive approach by which to process, prioritize, and interpret complex systems behavior from high-throughput data sets of theoretically unlimited size.Network biology approaches to proteomic data assessment and interpretation typically involve data reduction, clustering or grouping acquired data through ontological categorization, and a combination of pathway and complex network analysis (Figure).29,37–40 Functional ontology classification provides initial biological context for individual proteins, whereas pathway and network analysis integrate composition and architecture of associated interrelationships from defined proteomic observations. Both biological context and network architecture serve as tools to scrutinize acquired proteomes and to develop informed follow-up investigation and validation of insights predicted from high-throughput data.3,29,37–40Ontological CategorizationProteomic studies are proficient at detecting changes, yet biological interpretation is often limited by data complexity. When lists remain small, relevance can be anticipated and functionally validated on a protein-by-protein basis. For large-scale studies, this approach is untenable, and there is a need to define appropriate methodology for bioinformatic assessment of high-throughput data sets. Data comprehension is facilitated through clustering of proteins by biological processes, cellular components, or molecular functions (Figure).29,37–40,49 Accurate categorization requires the ability to cross-reference protein database information with complementary sources, such as the Gene Ontology (GO) or pathway analysis applications. Despite progress, lack of conformity in nomenclature and computational systems biology data and language formats can limit cross-platform interoperability.For spectral assignment during tandem MS (MS/MS) and initial protein characterization, the UniProt Knowledge Base (UniProtKB) is an established reference. Alternatives include or have included International Protein Index, Protein Information Resource, National Center for Biotechnology Information nonredundant database, Swiss-Prot (from the Swiss Institute of Bioinformatics), and Translated European Molecular Biology Laboratory Nucleotide Sequence Database from the European Bioinformatics Institute. The UniProt Consortium initially combined Protein Information Resource, Swiss-Prot, and Translated European Molecular Biology Laboratory into the UniProtKB and now also includes the International Protein Index database. UniProtKB is curated and continuously updated to incorporate new entries and minimize redundancies, while maintaining a history of entry changes, such as consolidation, replacement, or protein/gene renaming.UniProtKB entries often include multiple identifiers, increasing the ability to acquire complementary data from other repositories during subsequent analyses. They also contain a wealth of related information, such as interacting partners and residency in protein complexes or functional modules, GO information regarding associated molecular functions or biological processes, and hyperlinks to additional function- and interaction-related databases. Of the protein identifiers used in UniProtKB, the Human Genome Organization gene symbol ID is prioritized and universally applied across databases. Therefore, it serves as a pragmatic choice for cross-database and downstream querying of UniProtKB-derived proteomic information. Protein complexity, however, still occasionally leads to Human Genome Organization ID nomenclature issues during bioinformatic data assessment. This may occur when: (1) a single ID represents multiple variants of 1 protein, all of which may be subsumed by MS/MS data and thus indistinguishable from one another, eg, TNNT2 represents 10 human cardiac troponin T splice variants; (2) other highly homologous proteins are subsumed by spectral data, eg, actin isoforms; (3) MS/MS peptide matches are subsumed by protein isoforms of identical sequence arising through gene duplication, eg, calmodulins 1, 2, and 3; and (4) synonyms exist for a single protein, eg, histone 4 has 14 human and 12 mouse Human Genome Organization IDs. From each scenario, however, only 1 ID is assigned, in accordance with accepted parsimony standards.50 Although indistinguishable isoforms and ID redundancy might be considered confounding issues during ontological classification and pathway/network analysis, their impact is negligible because they are variants of the same protein, and their occurrence is infrequent in the context of hundreds of additional proteins.Most cardiovascular proteomic studies now include ontological categorization, clustering identified proteins by GO biological process, cellular component, or molecular function (Figure), with or without segregation into expression cohorts, ie, upregulated, downregulated, or unchanged. While providing initial biological context, assignment of a single function to each protein is often an oversimplification, because many proteins participate in multiple physiological processes. For instance, promiscuous enzymes influencing an array of other proteins and their activity, eg, oxidoreductases, kinases, and phosphatases, may carry out a specific function, such as phosphate transfer, yet this may impact numerous biological processes, confounding comprehensive protein categorization. In this regard, proteins with multiple GO associations are more appropriately accommodated and more effectively accounted for by calculating overrepresentation and enrichment significance during subsequent pathway and network analysis.Pathway Analysis AlgorithmsPathway analysis maps high-throughput data to, and assesses enrichment of, canonical pathways. Pathway algorithms facilitate data annotation, containing detailed descriptions of individual proteins and links to current knowledge. Pathway analysis software can also be used to calculate functional enrichment, by assessment of algorithm-specific functional terminology or GO molecular functions and biological processes. Some pathway algorithms enable biologically oriented network analysis, where subsets of proteins, represented as nodes or vertices, are connected by edges via established relationships, generating networks that may be further scrutinized for function or pathway overrepresentation. Thus, pathway analysis extends functional categorization by evaluating collective consequences of proteomic findings in the context of established pathways, functions, and interactions (Figure).Pathway analysis is an expanding field, with a diversity of bioinformatic applications designed to address molecular pathways and interactions. A comprehensive list of applications available at Pathguide51 outlines over 300 existing pathway and molecular relationship resources. Collectively, they encompass 8 subcategories, including protein-protein interactions, metabolic pathways, signaling pathways, pathway diagrams, transcription factor/gene regulatory networks, protein-compound interactions, genetic interaction networks, protein sequence focused databases, and 14 repositories related to topics not covered by the 8 major categories. Pathguide provides information about each resource, facilitating informed decisions regarding algorithm selection.Deconvolution of broad-based proteomic data is most readily accomplished with pathway resources that maximize information retrieval, such as Ingenuity Pathways Knowledge Base, archived in 5 Pathguide categories—protein- protein interactions, metabolic pathways, signaling pathways, transcription factors and gene regulatory networks, and protein-compound interactions. Ingenuity Pathways Knowledge Base is composed of data manually curated or automatically extracted from the literature and incorporates reviewed content from over 20 additional repositories. In turn, data are accessed and interrogated using the complementary bioinformatics tool, Ingenuity Pathways Analysis (IPA). Because Ingenuity Pathways Knowledge Base is a compilation of existing data, it is important to emphasize that IPA analysis is a pathway-oriented interpretation of current knowledge rather than inference of novel outcomes and interactions. As data acquisition increases, gaps in knowledge and limitations in scope will be reduced, expanding and refining pathway analysis interpretation. Together, IPA and Ingenuity Pathways Knowledge Base offer broad applicability and, as such, have become increasingly popular for cardiovascular proteomic studies,3,5,37–40,52–66 as have various alternative applications for ontology/enrichment analysis (eg, Gene Set Enrichment Analysis,67 Ontologizer,68 and the Database for Annotation, Visualization, and Integrated Discovery)68 and dedicated pathway and network analysis (eg, MetaCore’s GeneGo).69Pertinent considerations for implementing pathway analysis can be garnered from an overview of IPA search parameters. IPA accepts 21 different identifiers to process and interpret high-throughput datasets, from a number of gene/protein ID formats suitable for proteomic data, several mRNA microarray platforms, plus additional formats to facilitate microRNA or metabolomic studies, and, more recently, RNA-Seq and next generation sequencing experiments. Thus, pathway analysis can be carried out using proteomic data alone or integrated with other high-throughput data. Protein IDs uploaded for individual or multiple observations may include quantitative values such as fold change, P value, or log ratio. Additional user-specified variables include output data set size (eg, optional settings for maximum size and number of generated networks), desired stringency and scope (eg, species-specific information; consideration of direct and indirect relationships; inclusion of observed and predicted interactions; inclusion/exclusion of endogenous chemicals, together with genes/proteins as network nodes), whether or not to focus on a subset in isolation (eg, fold change and P value cutoffs to focus on up-, down-, or both up- and down-regulated proteins simultaneously; inclusion/exclusion of unaltered proteins from assessment), and selection of a baseline or standard reference set against which proteins of interest are compared for pathway and function enrichment or overrepresentation. To cast as wide a net as possible for detection of pathway and network interactions, both direct and indirect relationships across a variety of tissues and species should be included,37–40 as this will obviate annotation limitations. For instance, tissue-specific limits may be confounded by gene/protein annotation being based primarily on healthy tissues, which may not reflect observed expression under conditions of stress or disease, eg, fetal isoform expression in adult tissue, whereas setting species limits may overlook relevant knowledge of a particular protein that is more extensively documented in a species other than the one under investigation. Search stringency for proteomic data is better attained by limiting interpretation to include proteins only, by limiting interactions to those that are experimentally observed and by defining network generation parameters to minimize the number of networked molecules required to incorporate all submitted proteins.37–40As an evolving component of proteomic research, pathway analysis is best exploited as an interpretation tool facilitating hypothesis generation for subsequent follow-up, rather than as a confirmatory/validation step within the data analysis pipeline. The utility and potential of pathway analysis software are tempered by the fact that documented relationships only comprise partial biological information. Moreover, reliability of data supporting these interactions is variable, and it is impractical to assess interactions for quality control on a protein-by-protein basis when thousands of connections are generated. Finally, substantial effects on pathway software have been demonstrated by annotation changes, updates, additions, and errors, all of which impact prediction and significance outcomes.70 Thus, pathway analysis searches run the risk of being incomplete or potentially misleading and are best validated by experimental follow-up.Overrepresentation of normal and adverse biological functions assessed in IPA can be useful in guiding follow-up. In a model of imposed stress in the context of ATP-sensitive K+ channel deficiency, IPA analysis of proteomic data exclusively predicted cardiac adverse effects, which were subsequently validated by in vivo assessment in the ATP-sensitive K+ channel–deficient cohort.38 Adverse effect screening was also used in a study examining proteomic consequences of cardiomyopathy and structure/function remodeling mediated by stem cell therapy.40 In this regard, cardiomyopathic proteome changes predicted several cardiac adverse effects and overrepresentation of cardiac disease, which were diminished or no longer evident when assessing proteomic remodeling of stem cell–treated cardiomyopathic hearts. Validation of the predicted spectrum of structural and functional benefits by stem cell therapy was demonstrated by multimodal imaging, indicating restoration of prestress cardiac functional levels and normalization of heart structure.40 Thus, proteome- specified functional targets can be identified by pathway analysis for experimental follow-up and validation.Networks generated during pathway analysis also yield biologically relevant contexts for functional prioritization, hypothesis generation, and experimental validation (Figure). IPA networks are ranked by calculated overrepresentation of the proportion of input proteins within each network relative to expected numbers if proteins were instead randomly selected from a background reference set. Included with these prioritized networks is predicted intranetwork functional enrichment, which can further guide hypothesis generation. For example, the top prioritized network generated from proteomic analysis of endoderm secretome-mediated cardiopoiesis was associated with cardiovascular growth and development.37 Validation was carried out in silico by removal of the most highly connected network node from the list of input proteins in a parallel IPA assessment, which demoted cardiovascular development prioritization. Validation was independently conducted in vitro by targeted pharmacological inhibition, which abolished the potentiation of cardiac differentiation, thereby confirming network contributions to cardiopoiesis.37Default IPA constraints typically double the number of proteins from initial upload to network output,3,37–40 thereby increasing complexity, but this also provides additional targets for hypothesis generation by incorporating potentially relevant proteins not detected during initial proteomic screening that can be targeted, assessed, and validated.37 Another case in point is an IPA-derived network assessing crosstalk between endothelial and vascular smooth muscle cells in response to differential shear stress.64 Here, the primary proteomic network included several secretory proteins involved in cellular communication and function modulation. Some of these proteins were not detected during proteomic analysis, yet their presence in the network suggested criticality for intercellular crosstalk underlying observed vascular remodeling, which was subsequently assessed and validated.64 Thus, pathway analysis provides actionable predictions for validation via network functional prioritization, including addition of target proteins networked with, but not initially observed among, acquired proteomic data.Pathway algorithms are advantageous for enrichment analysis or function and ontology representation but lack tools necessary to characterize network architectural topology and positional relevance of network components. Pathway analysis networks emphasize biological relationships over structure and are designed to be sufficiently large to provide biological interactions, yet small enough for rapid network inference and construction, and to readily visualize all nodes simultaneously. IPA network settings, for instance, range from 35 to 140 nodes, and although multiple networks can be combined, merges may comprise no more than 500 nodes at a time. Moreover, no provisions are included to assess functional enrichment within merged composite interactomes. To fully comprehend network structure and composition for inclusive systems interpretation of proteomic data, particularly as data sets continue to increase in magnitude, it is necessary to move beyond pathway software to applications dedicated to network visualization and interpretation.Complex Network AnalysisComplex network analysis enables integrative proteome study across the spectrum of properties inherent within assembled protein-protein interrelationships. These mathematical representations comprise individual protein constituents, termed nodes or vertices in a derived network, held together by physical or regulatory interconnections, defined as edges (Table 1). Nodes and edges can be assessed for architectural connectivity, superimposed with qualitative or quantitative information, and, for some network algorithms, linked to external annotation or ontological information sources. Networks can also be modified or updated in an iterative fashion. Although popular for proteomic and other systems interpretation, network approaches are limited with respect to input data quality, output variability from differing algorithms, and lack of flexibility,22,71 with limited capacity to account for posttranslational or spatiotemporal dynamics, or when networking data acquired from multiple high-throughput sources. Metabolic networks, for example, may comprise protein nodes with metabolite edges, metabolite nodes with protein edges, or both proteins and metabolites as nodes connected by various interrelationships. Applied judiciously in light of such caveats, networks remain versatile platforms on which to integrate proteomic and other high-throughput data for systems comprehension.22Current understanding of the nonstochastic nature of networks arose from the demonstration that supposedly random networks from a variety of sources instead possess nonrandom connectivity.72 In these networks, the vast majority of nodes possess 1 or few edges, whereas an exceedingly small proportion are more highly connected. Node connectivity is defined as degree, the number of edges a node possesses, ie, how many other nodes it connects to in the network. Rather than exhibiting Gaussian node degree distributions, expected if randomly connected, networks approximate power law distributions that form characteristic scale-free topologies. Debate continues regarding whether network structures are scale-free, exponential, or adhere to more esoteric mathematical constraints, yet it is clear that their organization is not random. Nonstochasticity of network architecture is now known to be universally applicable, and this imparts emergent structural properties of biological relevance, such as network motifs, modularity, and functiona" @default.
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• W2155152257 title "Systems Proteomics for Translational Network Medicine" @default.
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