Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-01

AUTHORS

Da Wei Huang, Brad T Sherman, Richard A Lempicki

ABSTRACT

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies. More... »

PAGES

44

Journal

TITLE

Nature Protocols

ISSUE

1

VOLUME

4

Related Patents

  • Biomarkers For Lung Neuroendocrine Tumors
  • Biomarkers For Early Diagnosis Of Alzheimer's Disease
  • Computer-Implemented Systems And Methods For Data Management And Visualization
  • Inhibitors Of Cacna1a/Alpha1a Subunit Internal Ribosomal Entry Site (Ires) And Methods Of Treating Spinocerebellar Ataxia Type 6
  • Method And System For Extraction And Normalization Of Relationships Via Ontology Induction
  • Schematic And Database Linking System
  • Systems For Network Risk Assessment Including Processing Of User Access Rights Associated With A Network Of Devices
  • Systems For Computer Network Security Risk Assessment Including User Compromise Analysis Associated With A Network Of Devices
  • Network Anomaly Detection
  • Systems And Methods For Organizing And Identifying Documents Via Hierarchies And Dimensions Of Tags
  • Time-Sensitive Cube
  • Systems And Interactive User Interfaces For Automatic Generation Of Temporal Representation Of Data Objects
  • Taf1 Inhibitors For The Therapy Of Cancer
  • Compositions And Methods For Treatment Of Melanoma
  • Combinations Of Mdm2 Inhibitors And Bcl-Xl Inhibitors
  • Methods And Biomarkers For Analysis Of Colorectal Cancer
  • Microrna Treatment Of Fibrosis
  • Systems And User Interfaces For Dynamic And Interactive Investigation Of Bad Actor Behavior Based On Automatic Clustering Of Related Data In Various Data Structures
  • Alteration Of Neuronal Gene Expression By Synthetic Pirnas And By Alteration Of Pirna Function
  • System And Method Of Generating Data Points From One Or More Data Stores Of Data Items For Chart Creation And Manipulation
  • System And Method For Parameterizing Documents For Automatic Workflow Generation
  • Modulation Of Cardiac Stem-Progenitor Cell Differentiation, Assays And Uses Thereof
  • Systems And User Interfaces For Holistic, Data-Driven Investigation Of Bad Actor Behavior Based On Clustering And Scoring Of Related Data
  • Human Pluripotent Stem Cell-Based Models For Predictive Developmental Neural Toxicity
  • Genetic Markers Associated With Asd And Other Childhood Developmental Delay Disorders
  • Muscle Secretome And Uses Thereof
  • Compositions And Methods For Inhibiting Human Host Factors Required For Influenza Virus Replication
  • Generating Object Time Series And Data Objects
  • Interactive Data Object Map
  • System For Providing Dynamic Linked Panels In User Interface
  • Methods Of Treating Cancer Patients Responding To Ezh2 Inhibitor Gsk126
  • Synergistic Combinations Of Ox40l Antibodies For The Treatment Of Gvhd
  • Therapy For Mll-Rearranged Leukemia
  • Interactive User Interfaces For Location-Based Data Analysis
  • Differentiation Into Brown Adipocytes
  • Systems And Methods For Automatic And Customizable Data Minimization Of Electronic Data Stores
  • Molecular Signature Of Hepatocellular Carcinoma
  • Assays For Massively Combinatorial Perturbation Profiling And Cellular Circuit Reconstruction
  • Data Aggregation And Analysis System
  • Interactive User Interface For Dynamic Data Analysis Exploration And Query Processing
  • Methods, Systems And Kits For Predicting Viral Set Point
  • Computer-Implemented Systems And Methods For Data Management And Visualization
  • Systems For Network Risk Assessment Including Processing Of User Access Rights Associated With A Network Of Devices
  • Systems And User Interfaces For Dynamic And Interactive Simultaneous Querying Of Multiple Data Stores
  • Compounds For Inducing Anti-Tumor Immunity And Methods Thereof
  • Mir-155 Inhibitors For Treating Cutaneous T Cell Lymphoma (Ctcl)
  • Network Anomaly Detection
  • Interactive User Interface For Dynamic Data Analysis Exploration And Query Processing
  • Microbiome Response To Agents
  • System And Method For Generating Event Visualizations
  • Gene Expression Panel For Breast Cancer Prognosis
  • Oligonucleotide Compositions And Uses Thereof
  • Methods For Increasing Immune Responses Using Agents That Directly Bind To And Activate Ire-1
  • Compositions And Methods For Epithelial Stem Cell Expansion And Culture
  • Mobile Reports
  • Methods Of Derivation And/Or Propagation Of Epithelial Cells
  • Use Of Teams In A Mobile Application
  • Method For Classifying Tumour Cells
  • Methods And Compositions For Obtaining Useful Plant Traits
  • Biomarkers For The Detection Of Frontotemporal Dementia
  • Modal-Less Interface Enhancements
  • Immortalized Mesenchymal Stromal Cell From Adipose Tissue
  • Detection Of Oxidized Polypeptides
  • Simple Web Search
  • Compositions And Methods For Epithelial Stem Cell Expansion And Culture
  • External Malware Data Item Clustering And Analysis
  • Tactical Security System
  • System And Method For Batch Evaluation Programs
  • System For Providing Dynamic Linked Panels In User Interface
  • Targeting Metabolic Enzymes In Human Cancer
  • Tactical Security System
  • Compounds For Inducing Anti-Tumor Immunity And Methods Thereof
  • Mirna
  • Synergistic Combinations Of Ox40l Antibodies For The Treatment Of Gvhd
  • Time-Series Analysis System
  • Diagnosis And Treatment Of Metabolic Disorders
  • Network Intrusion Data Item Clustering And Analysis
  • Unwanted Tunneling Alert System
  • Alternatively Spliced Mrna Isoforms As Prognostic Indicators For Metastatic Cancer
  • Surface Markers For The Isolation Of Myogenic Stem/Progenitor Cells
  • Systems And User Interfaces For Dynamic And Interactive Access Of, Investigation Of, And Analysis Of Data Objects Stored In One Or More Databases
  • Malicious Software Detection In A Computing System
  • Systems For Network Risk Assessment Including Processing Of User Access Rights Associated With A Network Of Devices
  • Generating Object Time Series From Data Objects
  • Network Anomaly Detection
  • Interactive Geospatial Map
  • Concept Indexing Among Database Of Documents Using Machine Learning Techniques
  • Antisense Oligonucleotides Targeting 3'Utr Region Of A20
  • Systems And Methods For Key Phrase Characterization Of Documents
  • Long-Term Self-Renewing Neural Stem Cells
  • Gene Expression Profile In Diagnostics
  • Method Of Producing Renal Cells From Fibroblasts
  • Systems For Network Risk Assessment Including Processing Of User Access Rights Associated With A Network Of Devices
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nprot.2008.211

    DOI

    http://dx.doi.org/10.1038/nprot.2008.211

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039987283

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/19131956


    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/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computational Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Data Interpretation, Statistical", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genes", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Internet", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Software", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "name": [
                "Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Da Wei Huang", 
            "id": "sg:person.011667644635.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011667644635.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sherman", 
            "givenName": "Brad T", 
            "id": "sg:person.01322514467.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322514467.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lempicki", 
            "givenName": "Richard A", 
            "id": "sg:person.0716662073.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0716662073.55"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/gb-2003-4-10-r70", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001293245", 
              "https://doi.org/10.1186/gb-2003-4-10-r70"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btg455", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001761938"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkm415", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003264848"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkh409", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015906494"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-189", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016524045", 
              "https://doi.org/10.1186/1471-2105-6-189"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-189", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016524045", 
              "https://doi.org/10.1186/1471-2105-6-189"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2003-4-5-p3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021292424", 
              "https://doi.org/10.1186/gb-2003-4-5-p3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bth088", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022171874"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2007-8-9-r183", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027884417", 
              "https://doi.org/10.1186/gb-2007-8-9-r183"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.142287999", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027915405"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2004-5-12-r101", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030306020", 
              "https://doi.org/10.1186/gb-2004-5-12-r101"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gki454", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033041691"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0506580102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037705714"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0506580102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037705714"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-8-426", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037731520", 
              "https://doi.org/10.1186/1471-2105-8-426"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-8-426", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037731520", 
              "https://doi.org/10.1186/1471-2105-8-426"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-6-168", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047729779", 
              "https://doi.org/10.1186/1471-2105-6-168"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bti565", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050298763"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.6026/97320630002428", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1073593888"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2009-01", 
        "datePublishedReg": "2009-01-01", 
        "description": "DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/nprot.2008.211", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2427747", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1037502", 
            "issn": [
              "1754-2189", 
              "1750-2799"
            ], 
            "name": "Nature Protocols", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "4"
          }
        ], 
        "name": "Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources", 
        "pagination": "44", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "be7ba210f9eb9661b068cedd2c95dddc0aaf89057fdc8ccbbcc4557099756304"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "19131956"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "101284307"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/nprot.2008.211"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1039987283"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/nprot.2008.211", 
          "https://app.dimensions.ai/details/publication/pub.1039987283"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T13:00", 
        "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/v90/0000000001_0000000264/records_8659_00000429.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/nprot.2008.211"
      }
    ]
     

    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.10.1038/nprot.2008.211'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/nprot.2008.211'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nprot.2008.211'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nprot.2008.211'


     

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

    166 TRIPLES      21 PREDICATES      51 URIs      27 LITERALS      15 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/nprot.2008.211 schema:about N071ec21af69543eb989b20033d9bd554
    2 N09cef72ed74b4adc9016a6b522336099
    3 N72c3ff599dfa47f6a87e9256d4882f70
    4 N9096e6d0fc8a42b1a5ede223e55b2b00
    5 N9957510884ab429d8092cd0895bb9c11
    6 Nf4b7446b1a6345ffbf6d133c2db0e743
    7 anzsrc-for:06
    8 anzsrc-for:0604
    9 schema:author N16e812f2de1f4873a3c8056b75cabcbe
    10 schema:citation sg:pub.10.1186/1471-2105-6-168
    11 sg:pub.10.1186/1471-2105-6-189
    12 sg:pub.10.1186/1471-2105-8-426
    13 sg:pub.10.1186/gb-2003-4-10-r70
    14 sg:pub.10.1186/gb-2003-4-5-p3
    15 sg:pub.10.1186/gb-2004-5-12-r101
    16 sg:pub.10.1186/gb-2007-8-9-r183
    17 https://doi.org/10.1073/pnas.0506580102
    18 https://doi.org/10.1073/pnas.142287999
    19 https://doi.org/10.1093/bioinformatics/btg455
    20 https://doi.org/10.1093/bioinformatics/bth088
    21 https://doi.org/10.1093/bioinformatics/bti565
    22 https://doi.org/10.1093/nar/gkh409
    23 https://doi.org/10.1093/nar/gki454
    24 https://doi.org/10.1093/nar/gkm415
    25 https://doi.org/10.6026/97320630002428
    26 schema:datePublished 2009-01
    27 schema:datePublishedReg 2009-01-01
    28 schema:description DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
    29 schema:genre research_article
    30 schema:inLanguage en
    31 schema:isAccessibleForFree false
    32 schema:isPartOf N4151c7bffc9a431a9e3d836c6dad6cd6
    33 Nc9d5879285cf4d7d81d69700c9fa6ca1
    34 sg:journal.1037502
    35 schema:name Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
    36 schema:pagination 44
    37 schema:productId N51e6a9806e3e489fb4bf9f0b625fb390
    38 N899c7298065a44ec81606c73c87731f8
    39 Ncb5cd698a35d4b618a9a53b58d9f5ec9
    40 Ne3a923975a5d428583b2b0edbd89f20b
    41 Nfc399d364ace4b559d28a38eff0387c1
    42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039987283
    43 https://doi.org/10.1038/nprot.2008.211
    44 schema:sdDatePublished 2019-04-10T13:00
    45 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    46 schema:sdPublisher Nc63ec49979054923963133877f112432
    47 schema:url https://www.nature.com/articles/nprot.2008.211
    48 sgo:license sg:explorer/license/
    49 sgo:sdDataset articles
    50 rdf:type schema:ScholarlyArticle
    51 N01a44eb0d34f4f248ef4c92e67698218 rdf:first sg:person.01322514467.55
    52 rdf:rest Nd5387365587d41ce8caf450382936a99
    53 N071ec21af69543eb989b20033d9bd554 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    54 schema:name Genes
    55 rdf:type schema:DefinedTerm
    56 N09cef72ed74b4adc9016a6b522336099 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    57 schema:name Internet
    58 rdf:type schema:DefinedTerm
    59 N16e812f2de1f4873a3c8056b75cabcbe rdf:first sg:person.011667644635.83
    60 rdf:rest N01a44eb0d34f4f248ef4c92e67698218
    61 N3ef82822a1954d8d9c22eea2001a327d schema:name Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, USA.
    62 rdf:type schema:Organization
    63 N4151c7bffc9a431a9e3d836c6dad6cd6 schema:issueNumber 1
    64 rdf:type schema:PublicationIssue
    65 N51e6a9806e3e489fb4bf9f0b625fb390 schema:name nlm_unique_id
    66 schema:value 101284307
    67 rdf:type schema:PropertyValue
    68 N72c3ff599dfa47f6a87e9256d4882f70 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    69 schema:name Data Interpretation, Statistical
    70 rdf:type schema:DefinedTerm
    71 N7dc67ce9b74d403b8a3aa24a06353253 schema:name Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, USA.
    72 rdf:type schema:Organization
    73 N899c7298065a44ec81606c73c87731f8 schema:name doi
    74 schema:value 10.1038/nprot.2008.211
    75 rdf:type schema:PropertyValue
    76 N9096e6d0fc8a42b1a5ede223e55b2b00 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    77 schema:name Software
    78 rdf:type schema:DefinedTerm
    79 N9334260df0884c26a7dc0fe070069528 schema:name Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, Maryland 21702, USA.
    80 rdf:type schema:Organization
    81 N9957510884ab429d8092cd0895bb9c11 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    82 schema:name Computational Biology
    83 rdf:type schema:DefinedTerm
    84 Nc63ec49979054923963133877f112432 schema:name Springer Nature - SN SciGraph project
    85 rdf:type schema:Organization
    86 Nc9d5879285cf4d7d81d69700c9fa6ca1 schema:volumeNumber 4
    87 rdf:type schema:PublicationVolume
    88 Ncb5cd698a35d4b618a9a53b58d9f5ec9 schema:name dimensions_id
    89 schema:value pub.1039987283
    90 rdf:type schema:PropertyValue
    91 Nd5387365587d41ce8caf450382936a99 rdf:first sg:person.0716662073.55
    92 rdf:rest rdf:nil
    93 Ne3a923975a5d428583b2b0edbd89f20b schema:name readcube_id
    94 schema:value be7ba210f9eb9661b068cedd2c95dddc0aaf89057fdc8ccbbcc4557099756304
    95 rdf:type schema:PropertyValue
    96 Nf4b7446b1a6345ffbf6d133c2db0e743 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    97 schema:name Genomics
    98 rdf:type schema:DefinedTerm
    99 Nfc399d364ace4b559d28a38eff0387c1 schema:name pubmed_id
    100 schema:value 19131956
    101 rdf:type schema:PropertyValue
    102 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    103 schema:name Biological Sciences
    104 rdf:type schema:DefinedTerm
    105 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    106 schema:name Genetics
    107 rdf:type schema:DefinedTerm
    108 sg:grant.2427747 http://pending.schema.org/fundedItem sg:pub.10.1038/nprot.2008.211
    109 rdf:type schema:MonetaryGrant
    110 sg:journal.1037502 schema:issn 1750-2799
    111 1754-2189
    112 schema:name Nature Protocols
    113 rdf:type schema:Periodical
    114 sg:person.011667644635.83 schema:affiliation N7dc67ce9b74d403b8a3aa24a06353253
    115 schema:familyName Da Wei Huang
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011667644635.83
    117 rdf:type schema:Person
    118 sg:person.01322514467.55 schema:affiliation N3ef82822a1954d8d9c22eea2001a327d
    119 schema:familyName Sherman
    120 schema:givenName Brad T
    121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322514467.55
    122 rdf:type schema:Person
    123 sg:person.0716662073.55 schema:affiliation N9334260df0884c26a7dc0fe070069528
    124 schema:familyName Lempicki
    125 schema:givenName Richard A
    126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0716662073.55
    127 rdf:type schema:Person
    128 sg:pub.10.1186/1471-2105-6-168 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047729779
    129 https://doi.org/10.1186/1471-2105-6-168
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1186/1471-2105-6-189 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016524045
    132 https://doi.org/10.1186/1471-2105-6-189
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1186/1471-2105-8-426 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037731520
    135 https://doi.org/10.1186/1471-2105-8-426
    136 rdf:type schema:CreativeWork
    137 sg:pub.10.1186/gb-2003-4-10-r70 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001293245
    138 https://doi.org/10.1186/gb-2003-4-10-r70
    139 rdf:type schema:CreativeWork
    140 sg:pub.10.1186/gb-2003-4-5-p3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021292424
    141 https://doi.org/10.1186/gb-2003-4-5-p3
    142 rdf:type schema:CreativeWork
    143 sg:pub.10.1186/gb-2004-5-12-r101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030306020
    144 https://doi.org/10.1186/gb-2004-5-12-r101
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1186/gb-2007-8-9-r183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027884417
    147 https://doi.org/10.1186/gb-2007-8-9-r183
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1073/pnas.0506580102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037705714
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1073/pnas.142287999 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027915405
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1093/bioinformatics/btg455 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001761938
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1093/bioinformatics/bth088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022171874
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1093/bioinformatics/bti565 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050298763
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1093/nar/gkh409 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015906494
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1093/nar/gki454 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033041691
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1093/nar/gkm415 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003264848
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.6026/97320630002428 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073593888
    166 rdf:type schema:CreativeWork
     




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


    ...