Interrogation of genome-wide networks in biology: comparison of knowledge-based and statistical methods View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01-01

AUTHORS

Sathyabaarathi Ravichandran, Nagasuma Chandra

ABSTRACT

Networks are used extensively in the study of biological systems to address a wide range of questions such as understanding the complex behaviour of a given system or identifying key alterations leading to a disease phenotype. Numerous network-based methods have been developed for inferring molecular interactions using transcriptomic and proteomic data. Different network methods come with their own advantages and limitations, and often give different results for the same data. A systematic study is essential to understand how the methods fare in terms of correctly predicting known biological processes and yielding testable biological hypotheses. To address this, we have carried out a comparison of four different methods to derive context-specific perturbations for two different case studies and evaluated their performance. The methods can be broadly classified into statistical inference and knowledge-based methods. Two of the four methods, WGCNA and ARACNE, belong to the broad class of data-driven approaches which do not rely on prior network information. On the other hand, ResponseNet and jActiveModules utilise knowledge-based protein–protein interaction networks and integrate condition-specific transcriptome or proteome data. We evaluated the interactions inferred through all the approaches and assessed their biological relevance based on three criteria: (1) enrichment of the gold standard gene sets, (2) comparison to gold standard pathways and (3) recovery of hub genes from the context-specific perturbed network, known to be related to the given condition. Comparing the performance of these four methods in two different cases, tuberculosis and melanoma, showed superior performance by ResponseNet, based on all three criteria. More... »

PAGES

1-19

References to SciGraph publications

  • 2009-12. A module-based analytical strategy to identify novel disease-associated genes shows an inhibitory role for interleukin 7 Receptor in allergic inflammation in BMC SYSTEMS BIOLOGY
  • 2010-10. Advantages and limitations of current network inference methods in NATURE REVIEWS MICROBIOLOGY
  • 2008-12. WGCNA: an R package for weighted correlation network analysis in BMC BIOINFORMATICS
  • 2013-12. Mining large-scale response networks reveals ‘topmost activities’ in Mycobacterium tuberculosis infection in SCIENTIFIC REPORTS
  • 2008-11. Network pharmacology: the next paradigm in drug discovery in NATURE CHEMICAL BIOLOGY
  • 2008-12. minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information in BMC BIOINFORMATICS
  • 2004-02. Network biology: understanding the cell's functional organization in NATURE REVIEWS GENETICS
  • 2005. limma: Linear Models for Microarray Data in BIOINFORMATICS AND COMPUTATIONAL BIOLOGY SOLUTIONS USING R AND BIOCONDUCTOR
  • 2008-12. Background correction using dinucleotide affinities improves the performance of GCRMA in BMC BIOINFORMATICS
  • 2013-11. Transcriptome Profiling Identifies HMGA2 as a Biomarker of Melanoma Progression and Prognosis in JOURNAL OF INVESTIGATIVE DERMATOLOGY
  • 2017-03. Prior knowledge guided active modules identification: an integrated multi-objective approach in BMC SYSTEMS BIOLOGY
  • 2017-12. Meta-analysis of host response networks identifies a common core in tuberculosis in NPJ SYSTEMS BIOLOGY AND APPLICATIONS
  • 2011-01. Network medicine: a network-based approach to human disease in NATURE REVIEWS GENETICS
  • 2005-10. Towards a proteome-scale map of the human protein–protein interaction network in NATURE
  • 2007-10. Integration of biological networks and gene expression data using Cytoscape in NATURE PROTOCOLS
  • 2011. Tackling the DREAM Challenge for Gene Regulatory Networks Reverse Engineering in AI*IA 2011: ARTIFICIAL INTELLIGENCE AROUND MAN AND BEYOND
  • 2006-03. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context in BMC BIOINFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12572-018-0242-9

    DOI

    http://dx.doi.org/10.1007/s12572-018-0242-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111050315


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0806", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information Systems", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Indian Institute of Science Bangalore", 
              "id": "https://www.grid.ac/institutes/grid.34980.36", 
              "name": [
                "IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka, India", 
                "Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ravichandran", 
            "givenName": "Sathyabaarathi", 
            "id": "sg:person.01051473157.53", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051473157.53"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Indian Institute of Science Bangalore", 
              "id": "https://www.grid.ac/institutes/grid.34980.36", 
              "name": [
                "IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka, India", 
                "Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chandra", 
            "givenName": "Nagasuma", 
            "id": "sg:person.0772712660.54", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0772712660.54"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.ygeno.2014.03.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000734461"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1039/c1mb05340j", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001229802"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkm902", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001424522"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.smim.2014.10.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001690068"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btp101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001910376"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nchembio.118", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006687583", 
              "https://doi.org/10.1038/nchembio.118"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature04209", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006733745", 
              "https://doi.org/10.1038/nature04209"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature04209", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006733745", 
              "https://doi.org/10.1038/nature04209"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/jid.2013.197", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007979822", 
              "https://doi.org/10.1038/jid.2013.197"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cell.2005.08.029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008327797"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkv1070", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010056536"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.bbamcr.2016.08.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010140903"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro2419", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010622335", 
              "https://doi.org/10.1038/nrmicro2419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro2419", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010622335", 
              "https://doi.org/10.1038/nrmicro2419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bth112", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012240750"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btr136", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012340855"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg1272", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018231980", 
              "https://doi.org/10.1038/nrg1272"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg1272", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018231980", 
              "https://doi.org/10.1038/nrg1272"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.071852.107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018391234"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1074/mcp.m400110-mcp200", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018718583"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btg1037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019135637"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-9-559", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020312314", 
              "https://doi.org/10.1186/1471-2105-9-559"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2202/1544-6115.1128", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020363278"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/database/bat018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020516517"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0029348", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020621681"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep02302", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020951792", 
              "https://doi.org/10.1038/srep02302"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg2918", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021371713", 
              "https://doi.org/10.1038/nrg2918"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg2918", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021371713", 
              "https://doi.org/10.1038/nrg2918"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-23954-0_34", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021570233", 
              "https://doi.org/10.1007/978-3-642-23954-0_34"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/0-387-29362-0_23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025432622", 
              "https://doi.org/10.1007/0-387-29362-0_23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1752-0509-3-19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028824534", 
              "https://doi.org/10.1186/1752-0509-3-19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1038/msb4100158", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036456248"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1038/msb4100158", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036456248"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gki072", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037000820"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/18.suppl_1.s233", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038177541"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0073230", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040010732"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-14-2959", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041485078"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-9-461", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041931207", 
              "https://doi.org/10.1186/1471-2105-9-461"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkw1102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041939167"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btm294", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042359859"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.073601.107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043119523"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkw943", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043223154"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2007.324", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043279308", 
              "https://doi.org/10.1038/nprot.2007.324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bti014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043974791"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0026938", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045036646"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gks1094", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045624288"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-9-452", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046437847", 
              "https://doi.org/10.1186/1471-2105-9-452"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fgene.2014.00299", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047405205"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btm554", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049253397"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-7-s1-s7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051833905", 
              "https://doi.org/10.1186/1471-2105-7-s1-s7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.1239303", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052744398"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/infdis/jiv238", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059709732"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1517/14622416.3.4.507", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067587735"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41540-017-0005-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083753657", 
              "https://doi.org/10.1038/s41540-017-0005-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12918-017-0388-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084251089", 
              "https://doi.org/10.1186/s12918-017-0388-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12918-017-0388-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084251089", 
              "https://doi.org/10.1186/s12918-017-0388-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/pcmr.12661", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092302803"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkx1064", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092391316"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.2517-6161.1995.tb02031.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110458929"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.2517-6161.1995.tb02031.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110458929"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-01-01", 
        "datePublishedReg": "2019-01-01", 
        "description": "Networks are used extensively in the study of biological systems to address a wide range of questions such as understanding the complex behaviour of a given system or identifying key alterations leading to a disease phenotype. Numerous network-based methods have been developed for inferring molecular interactions using transcriptomic and proteomic data. Different network methods come with their own advantages and limitations, and often give different results for the same data. A systematic study is essential to understand how the methods fare in terms of correctly predicting known biological processes and yielding testable biological hypotheses. To address this, we have carried out a comparison of four different methods to derive context-specific perturbations for two different case studies and evaluated their performance. The methods can be broadly classified into statistical inference and knowledge-based methods. Two of the four methods, WGCNA and ARACNE, belong to the broad class of data-driven approaches which do not rely on prior network information. On the other hand, ResponseNet and jActiveModules utilise knowledge-based protein\u2013protein interaction networks and integrate condition-specific transcriptome or proteome data. We evaluated the interactions inferred through all the approaches and assessed their biological relevance based on three criteria: (1) enrichment of the gold standard gene sets, (2) comparison to gold standard pathways and (3) recovery of hub genes from the context-specific perturbed network, known to be related to the given condition. Comparing the performance of these four methods in two different cases, tuberculosis and melanoma, showed superior performance by ResponseNet, based on all three criteria.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s12572-018-0242-9", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1050051", 
            "issn": [
              "0975-0770", 
              "0975-5616"
            ], 
            "name": "International Journal of Advances in Engineering Sciences and Applied Mathematics", 
            "type": "Periodical"
          }
        ], 
        "name": "Interrogation of genome-wide networks in biology: comparison of knowledge-based and statistical methods", 
        "pagination": "1-19", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "eeb8a1ee8cb5a62cbfdfd9ce008715584e98b5689855bc83924cc8c3899868fc"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s12572-018-0242-9"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1111050315"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s12572-018-0242-9", 
          "https://app.dimensions.ai/details/publication/pub.1111050315"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T08:32", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000309_0000000309/records_106291_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs12572-018-0242-9"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12572-018-0242-9'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12572-018-0242-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12572-018-0242-9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12572-018-0242-9'


     

    This table displays all metadata directly associated to this object as RDF triples.

    239 TRIPLES      21 PREDICATES      77 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s12572-018-0242-9 schema:about anzsrc-for:08
    2 anzsrc-for:0806
    3 schema:author Nde2fcaa456f644deb7482a4361ba5ffe
    4 schema:citation sg:pub.10.1007/0-387-29362-0_23
    5 sg:pub.10.1007/978-3-642-23954-0_34
    6 sg:pub.10.1038/jid.2013.197
    7 sg:pub.10.1038/nature04209
    8 sg:pub.10.1038/nchembio.118
    9 sg:pub.10.1038/nprot.2007.324
    10 sg:pub.10.1038/nrg1272
    11 sg:pub.10.1038/nrg2918
    12 sg:pub.10.1038/nrmicro2419
    13 sg:pub.10.1038/s41540-017-0005-4
    14 sg:pub.10.1038/srep02302
    15 sg:pub.10.1186/1471-2105-7-s1-s7
    16 sg:pub.10.1186/1471-2105-9-452
    17 sg:pub.10.1186/1471-2105-9-461
    18 sg:pub.10.1186/1471-2105-9-559
    19 sg:pub.10.1186/1752-0509-3-19
    20 sg:pub.10.1186/s12918-017-0388-2
    21 https://doi.org/10.1016/j.bbamcr.2016.08.007
    22 https://doi.org/10.1016/j.cell.2005.08.029
    23 https://doi.org/10.1016/j.smim.2014.10.002
    24 https://doi.org/10.1016/j.ygeno.2014.03.004
    25 https://doi.org/10.1038/msb4100158
    26 https://doi.org/10.1039/c1mb05340j
    27 https://doi.org/10.1074/mcp.m400110-mcp200
    28 https://doi.org/10.1093/bioinformatics/18.suppl_1.s233
    29 https://doi.org/10.1093/bioinformatics/btg1037
    30 https://doi.org/10.1093/bioinformatics/bth112
    31 https://doi.org/10.1093/bioinformatics/bti014
    32 https://doi.org/10.1093/bioinformatics/btm294
    33 https://doi.org/10.1093/bioinformatics/btm554
    34 https://doi.org/10.1093/bioinformatics/btp101
    35 https://doi.org/10.1093/bioinformatics/btr136
    36 https://doi.org/10.1093/database/bat018
    37 https://doi.org/10.1093/infdis/jiv238
    38 https://doi.org/10.1093/nar/gki072
    39 https://doi.org/10.1093/nar/gkm902
    40 https://doi.org/10.1093/nar/gks1094
    41 https://doi.org/10.1093/nar/gkv1070
    42 https://doi.org/10.1093/nar/gkw1102
    43 https://doi.org/10.1093/nar/gkw943
    44 https://doi.org/10.1093/nar/gkx1064
    45 https://doi.org/10.1101/gr.071852.107
    46 https://doi.org/10.1101/gr.073601.107
    47 https://doi.org/10.1101/gr.1239303
    48 https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
    49 https://doi.org/10.1111/pcmr.12661
    50 https://doi.org/10.1158/0008-5472.can-14-2959
    51 https://doi.org/10.1371/journal.pone.0026938
    52 https://doi.org/10.1371/journal.pone.0029348
    53 https://doi.org/10.1371/journal.pone.0073230
    54 https://doi.org/10.1517/14622416.3.4.507
    55 https://doi.org/10.2202/1544-6115.1128
    56 https://doi.org/10.3389/fgene.2014.00299
    57 schema:datePublished 2019-01-01
    58 schema:datePublishedReg 2019-01-01
    59 schema:description Networks are used extensively in the study of biological systems to address a wide range of questions such as understanding the complex behaviour of a given system or identifying key alterations leading to a disease phenotype. Numerous network-based methods have been developed for inferring molecular interactions using transcriptomic and proteomic data. Different network methods come with their own advantages and limitations, and often give different results for the same data. A systematic study is essential to understand how the methods fare in terms of correctly predicting known biological processes and yielding testable biological hypotheses. To address this, we have carried out a comparison of four different methods to derive context-specific perturbations for two different case studies and evaluated their performance. The methods can be broadly classified into statistical inference and knowledge-based methods. Two of the four methods, WGCNA and ARACNE, belong to the broad class of data-driven approaches which do not rely on prior network information. On the other hand, ResponseNet and jActiveModules utilise knowledge-based protein–protein interaction networks and integrate condition-specific transcriptome or proteome data. We evaluated the interactions inferred through all the approaches and assessed their biological relevance based on three criteria: (1) enrichment of the gold standard gene sets, (2) comparison to gold standard pathways and (3) recovery of hub genes from the context-specific perturbed network, known to be related to the given condition. Comparing the performance of these four methods in two different cases, tuberculosis and melanoma, showed superior performance by ResponseNet, based on all three criteria.
    60 schema:genre research_article
    61 schema:inLanguage en
    62 schema:isAccessibleForFree false
    63 schema:isPartOf sg:journal.1050051
    64 schema:name Interrogation of genome-wide networks in biology: comparison of knowledge-based and statistical methods
    65 schema:pagination 1-19
    66 schema:productId N45a9c04354084b6989e0fcb831881c6f
    67 N858bfc8cbff545d08b7db8ab34674dee
    68 N9cda9535f8f94fb4a4f422b2362977c8
    69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111050315
    70 https://doi.org/10.1007/s12572-018-0242-9
    71 schema:sdDatePublished 2019-04-11T08:32
    72 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    73 schema:sdPublisher N2c5e281c654c4e188a1f9cd867fed9f9
    74 schema:url https://link.springer.com/10.1007%2Fs12572-018-0242-9
    75 sgo:license sg:explorer/license/
    76 sgo:sdDataset articles
    77 rdf:type schema:ScholarlyArticle
    78 N2c5e281c654c4e188a1f9cd867fed9f9 schema:name Springer Nature - SN SciGraph project
    79 rdf:type schema:Organization
    80 N45a9c04354084b6989e0fcb831881c6f schema:name dimensions_id
    81 schema:value pub.1111050315
    82 rdf:type schema:PropertyValue
    83 N858bfc8cbff545d08b7db8ab34674dee schema:name readcube_id
    84 schema:value eeb8a1ee8cb5a62cbfdfd9ce008715584e98b5689855bc83924cc8c3899868fc
    85 rdf:type schema:PropertyValue
    86 N9cda9535f8f94fb4a4f422b2362977c8 schema:name doi
    87 schema:value 10.1007/s12572-018-0242-9
    88 rdf:type schema:PropertyValue
    89 Nde2fcaa456f644deb7482a4361ba5ffe rdf:first sg:person.01051473157.53
    90 rdf:rest Nf482435b80774592bbfb8bd2d54960fc
    91 Nf482435b80774592bbfb8bd2d54960fc rdf:first sg:person.0772712660.54
    92 rdf:rest rdf:nil
    93 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    94 schema:name Information and Computing Sciences
    95 rdf:type schema:DefinedTerm
    96 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
    97 schema:name Information Systems
    98 rdf:type schema:DefinedTerm
    99 sg:journal.1050051 schema:issn 0975-0770
    100 0975-5616
    101 schema:name International Journal of Advances in Engineering Sciences and Applied Mathematics
    102 rdf:type schema:Periodical
    103 sg:person.01051473157.53 schema:affiliation https://www.grid.ac/institutes/grid.34980.36
    104 schema:familyName Ravichandran
    105 schema:givenName Sathyabaarathi
    106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01051473157.53
    107 rdf:type schema:Person
    108 sg:person.0772712660.54 schema:affiliation https://www.grid.ac/institutes/grid.34980.36
    109 schema:familyName Chandra
    110 schema:givenName Nagasuma
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0772712660.54
    112 rdf:type schema:Person
    113 sg:pub.10.1007/0-387-29362-0_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025432622
    114 https://doi.org/10.1007/0-387-29362-0_23
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1007/978-3-642-23954-0_34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021570233
    117 https://doi.org/10.1007/978-3-642-23954-0_34
    118 rdf:type schema:CreativeWork
    119 sg:pub.10.1038/jid.2013.197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007979822
    120 https://doi.org/10.1038/jid.2013.197
    121 rdf:type schema:CreativeWork
    122 sg:pub.10.1038/nature04209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006733745
    123 https://doi.org/10.1038/nature04209
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1038/nchembio.118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006687583
    126 https://doi.org/10.1038/nchembio.118
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1038/nprot.2007.324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043279308
    129 https://doi.org/10.1038/nprot.2007.324
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1038/nrg1272 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018231980
    132 https://doi.org/10.1038/nrg1272
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1038/nrg2918 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021371713
    135 https://doi.org/10.1038/nrg2918
    136 rdf:type schema:CreativeWork
    137 sg:pub.10.1038/nrmicro2419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010622335
    138 https://doi.org/10.1038/nrmicro2419
    139 rdf:type schema:CreativeWork
    140 sg:pub.10.1038/s41540-017-0005-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083753657
    141 https://doi.org/10.1038/s41540-017-0005-4
    142 rdf:type schema:CreativeWork
    143 sg:pub.10.1038/srep02302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020951792
    144 https://doi.org/10.1038/srep02302
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1186/1471-2105-7-s1-s7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051833905
    147 https://doi.org/10.1186/1471-2105-7-s1-s7
    148 rdf:type schema:CreativeWork
    149 sg:pub.10.1186/1471-2105-9-452 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046437847
    150 https://doi.org/10.1186/1471-2105-9-452
    151 rdf:type schema:CreativeWork
    152 sg:pub.10.1186/1471-2105-9-461 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041931207
    153 https://doi.org/10.1186/1471-2105-9-461
    154 rdf:type schema:CreativeWork
    155 sg:pub.10.1186/1471-2105-9-559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020312314
    156 https://doi.org/10.1186/1471-2105-9-559
    157 rdf:type schema:CreativeWork
    158 sg:pub.10.1186/1752-0509-3-19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028824534
    159 https://doi.org/10.1186/1752-0509-3-19
    160 rdf:type schema:CreativeWork
    161 sg:pub.10.1186/s12918-017-0388-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084251089
    162 https://doi.org/10.1186/s12918-017-0388-2
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1016/j.bbamcr.2016.08.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010140903
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1016/j.cell.2005.08.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008327797
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1016/j.smim.2014.10.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001690068
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1016/j.ygeno.2014.03.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000734461
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1038/msb4100158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036456248
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1039/c1mb05340j schema:sameAs https://app.dimensions.ai/details/publication/pub.1001229802
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1074/mcp.m400110-mcp200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018718583
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1093/bioinformatics/18.suppl_1.s233 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038177541
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1093/bioinformatics/btg1037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019135637
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1093/bioinformatics/bth112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012240750
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1093/bioinformatics/bti014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043974791
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1093/bioinformatics/btm294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042359859
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1093/bioinformatics/btm554 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049253397
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1093/bioinformatics/btp101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001910376
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1093/bioinformatics/btr136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012340855
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1093/database/bat018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020516517
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1093/infdis/jiv238 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059709732
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1093/nar/gki072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037000820
    199 rdf:type schema:CreativeWork
    200 https://doi.org/10.1093/nar/gkm902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001424522
    201 rdf:type schema:CreativeWork
    202 https://doi.org/10.1093/nar/gks1094 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045624288
    203 rdf:type schema:CreativeWork
    204 https://doi.org/10.1093/nar/gkv1070 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010056536
    205 rdf:type schema:CreativeWork
    206 https://doi.org/10.1093/nar/gkw1102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041939167
    207 rdf:type schema:CreativeWork
    208 https://doi.org/10.1093/nar/gkw943 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043223154
    209 rdf:type schema:CreativeWork
    210 https://doi.org/10.1093/nar/gkx1064 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092391316
    211 rdf:type schema:CreativeWork
    212 https://doi.org/10.1101/gr.071852.107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018391234
    213 rdf:type schema:CreativeWork
    214 https://doi.org/10.1101/gr.073601.107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043119523
    215 rdf:type schema:CreativeWork
    216 https://doi.org/10.1101/gr.1239303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052744398
    217 rdf:type schema:CreativeWork
    218 https://doi.org/10.1111/j.2517-6161.1995.tb02031.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1110458929
    219 rdf:type schema:CreativeWork
    220 https://doi.org/10.1111/pcmr.12661 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092302803
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1158/0008-5472.can-14-2959 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041485078
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1371/journal.pone.0026938 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045036646
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1371/journal.pone.0029348 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020621681
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.1371/journal.pone.0073230 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040010732
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.1517/14622416.3.4.507 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067587735
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.2202/1544-6115.1128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020363278
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.3389/fgene.2014.00299 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047405205
    235 rdf:type schema:CreativeWork
    236 https://www.grid.ac/institutes/grid.34980.36 schema:alternateName Indian Institute of Science Bangalore
    237 schema:name Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India
    238 IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka, India
    239 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...