An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-10

AUTHORS

Balazs Györffy, Andras Lanczky, Aron C. Eklund, Carsten Denkert, Jan Budczies, Qiyuan Li, Zoltan Szallasi

ABSTRACT

Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources. More... »

PAGES

725-731

References to SciGraph publications

  • 2009-12. Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients in BREAST CANCER RESEARCH AND TREATMENT
  • 2008-12. The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis in BMC MEDICAL GENOMICS
  • 2008-12. Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen in BMC GENOMICS
  • 2009-12. The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1–98 trial in BMC MEDICAL GENOMICS
  • 2006-09. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements in NATURE BIOTECHNOLOGY
  • 2009-06. Genes that mediate breast cancer metastasis to the brain in NATURE
  • 2001-06. Estrogen receptor analysis in primary breast tumors by ligand-binding assay, immunocytochemical assay, and northern blot: a comparison in BREAST CANCER RESEARCH AND TREATMENT
  • 2009-07. The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy in BREAST CANCER RESEARCH AND TREATMENT
  • 2007-02. Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients in BREAST CANCER RESEARCH
  • 2005-12. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts in BREAST CANCER RESEARCH
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10549-009-0674-9

    DOI

    http://dx.doi.org/10.1007/s10549-009-0674-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1019543499

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/20020197


    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/2008/1112", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Oncology and Carcinogenesis", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Adult", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Aged", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Biomarkers, Tumor", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Breast Neoplasms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computer Graphics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Disease-Free Survival", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Female", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Profiling", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Regulation, Neoplastic", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genetic Markers", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Internet", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Kaplan-Meier Estimate", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Lymphatic Metastasis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Middle Aged", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Neoplasm Staging", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Oligonucleotide Array Sequence Analysis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Online Systems", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Predictive Value of Tests", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Prognosis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Receptors, Estrogen", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Reproducibility of Results", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Time Factors", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Semmelweis University", 
              "id": "https://www.grid.ac/institutes/grid.11804.3c", 
              "name": [
                "Joint Research Laboratory of the Hungarian Academy of Sciences and the Semmelweis University, Semmelweis University 1st Department of Pediatrics, Bokay u. 53-54, 1083, Budapest, Hungary"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gy\u00f6rffy", 
            "givenName": "Balazs", 
            "id": "sg:person.0707551766.31", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707551766.31"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "E\u00f6tv\u00f6s Lor\u00e1nd University", 
              "id": "https://www.grid.ac/institutes/grid.5591.8", 
              "name": [
                "Joint Research Laboratory of the Hungarian Academy of Sciences and the Semmelweis University, Semmelweis University 1st Department of Pediatrics, Bokay u. 53-54, 1083, Budapest, Hungary", 
                "Pazmany Peter University, Budapest, Hungary"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lanczky", 
            "givenName": "Andras", 
            "id": "sg:person.0644146035.65", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644146035.65"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Technical University of Denmark", 
              "id": "https://www.grid.ac/institutes/grid.5170.3", 
              "name": [
                "Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Eklund", 
            "givenName": "Aron C.", 
            "id": "sg:person.0777444624.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777444624.40"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Charit\u00e9", 
              "id": "https://www.grid.ac/institutes/grid.6363.0", 
              "name": [
                "Charit\u00e9 Universitaetsmedizin, Berlin, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Denkert", 
            "givenName": "Carsten", 
            "id": "sg:person.01052576706.33", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052576706.33"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Charit\u00e9", 
              "id": "https://www.grid.ac/institutes/grid.6363.0", 
              "name": [
                "Charit\u00e9 Universitaetsmedizin, Berlin, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Budczies", 
            "givenName": "Jan", 
            "id": "sg:person.01015122154.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01015122154.36"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Technical University of Denmark", 
              "id": "https://www.grid.ac/institutes/grid.5170.3", 
              "name": [
                "Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Li", 
            "givenName": "Qiyuan", 
            "id": "sg:person.01101666715.53", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101666715.53"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Harvard University", 
              "id": "https://www.grid.ac/institutes/grid.38142.3c", 
              "name": [
                "Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark", 
                "Children\u2019s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, MA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Szallasi", 
            "givenName": "Zoltan", 
            "id": "sg:person.0767122747.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0767122747.55"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1200/jco.2005.06.178", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001535254"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-008-0242-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002015079", 
              "https://doi.org/10.1007/s10549-008-0242-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-008-0242-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002015079", 
              "https://doi.org/10.1007/s10549-008-0242-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0506230102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002515049"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1200/jco.2006.07.1522", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003206258"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btg405", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003878496"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-05-4414", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008978686"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-9-239", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010596045", 
              "https://doi.org/10.1186/1471-2164-9-239"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature08021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010946448", 
              "https://doi.org/10.1038/nature08021"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature08021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010946448", 
              "https://doi.org/10.1038/nature08021"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1200/jco.2005.04.7985", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011750427"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/annonc/mdi352", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015874660"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-07-5206", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017296025"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkg763", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019401433"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1056/nejmoa041588", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022156409"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr1325", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023450491", 
              "https://doi.org/10.1186/bcr1325"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr1325", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023450491", 
              "https://doi.org/10.1186/bcr1325"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1755-8794-2-40", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025616151", 
              "https://doi.org/10.1186/1755-8794-2-40"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1470-2045(07)70042-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027417986"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0701138104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028426046"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/djj052", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030644591"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1078-0432.ccr-06-2765", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030779547"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0005645", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033676042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1755-8794-1-42", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034319319", 
              "https://doi.org/10.1186/1755-8794-1-42"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1017946810277", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035809985", 
              "https://doi.org/10.1023/a:1017946810277"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1239", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037875102", 
              "https://doi.org/10.1038/nbt1239"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1239", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037875102", 
              "https://doi.org/10.1038/nbt1239"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-008-0183-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038471868", 
              "https://doi.org/10.1007/s10549-008-0183-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-008-0183-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038471868", 
              "https://doi.org/10.1007/s10549-008-0183-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1677/erc-08-0338", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038686358"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tig.2005.12.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044109724"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr1639", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044880794", 
              "https://doi.org/10.1186/bcr1639"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr1639", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044880794", 
              "https://doi.org/10.1186/bcr1639"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0140-6736(05)17947-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047788005"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1200/jco.2007.14.2364", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049142592"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/01621459.1958.10501452", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058299418"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3892/ijo.20.4.791", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1071512157"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1200/jco.2001.19.4.980", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1074754816"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1075314221", 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2010-10", 
        "datePublishedReg": "2010-10-01", 
        "description": "Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10549-009-0674-9", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1092777", 
            "issn": [
              "0167-6806", 
              "1573-7217"
            ], 
            "name": "Breast Cancer Research and Treatment", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "123"
          }
        ], 
        "name": "An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients", 
        "pagination": "725-731", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "5d1e0e0c76f52a01f21cf1840edd199dc45dc433bc031b47f749def47d45502d"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "20020197"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "8111104"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10549-009-0674-9"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1019543499"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10549-009-0674-9", 
          "https://app.dimensions.ai/details/publication/pub.1019543499"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T09:56", 
        "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/v90/0000000347_0000000347/records_89804_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs10549-009-0674-9"
      }
    ]
     

    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/s10549-009-0674-9'

    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/s10549-009-0674-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10549-009-0674-9'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10549-009-0674-9'


     

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

    325 TRIPLES      21 PREDICATES      85 URIs      44 LITERALS      32 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10549-009-0674-9 schema:about N0a25496d5cd643a3a7d5c47097a52bb4
    2 N0ec44ce507c04f5dbaca1262f727e9e7
    3 N10238f0aa19d44e1943a10a5380c6b67
    4 N168a1cc871994e0ca4a7d0398b57e126
    5 N18c770c4bfff40d49fe6c2ebbc803ca9
    6 N1d15a6f4531244ae8e0cf77d2979cc31
    7 N21aeca36fd764709b12e7e7dbbed728c
    8 N2336018e156b4e48b2fb9e18b7bba1f1
    9 N27e7baf5ad7c4e86afbaba1dc8ed313c
    10 N3e39cd644a6d44eab9a3bc61106811be
    11 N5cd73f9986fc4b3080cb04b091a48cd0
    12 N68d5cf1b11e045e9b9e2586f9a7cf53c
    13 N97ad1278f25344fb88873154909c437c
    14 N9bd45cc018b848ffb453ad9ae9584123
    15 Na991ec0a0b1845ce96a3bccf52c89a28
    16 Nb272bfa7e6e94d65b79e5627e4bf890c
    17 Nb6eed43842b8465fb3b173bdb4f4f863
    18 Nbe13a18c8aba4f08ac461166f1592a1f
    19 Nc34b802bcfa04a13a40cb0f3fa74351a
    20 Nd144e535aa394783a55b000b3c2833b2
    21 Ned15d6ca385f4a0c9328cd29ed67a3e6
    22 Nfbd4d55ed3fd4bbe8d7f2d2580bd8615
    23 Nff29c95ba97a44e0921c8003626dcc15
    24 anzsrc-for:11
    25 anzsrc-for:1112
    26 schema:author N6895688fbdc947e7a29a3ea0208d579d
    27 schema:citation sg:pub.10.1007/s10549-008-0183-2
    28 sg:pub.10.1007/s10549-008-0242-8
    29 sg:pub.10.1023/a:1017946810277
    30 sg:pub.10.1038/nature08021
    31 sg:pub.10.1038/nbt1239
    32 sg:pub.10.1186/1471-2164-9-239
    33 sg:pub.10.1186/1755-8794-1-42
    34 sg:pub.10.1186/1755-8794-2-40
    35 sg:pub.10.1186/bcr1325
    36 sg:pub.10.1186/bcr1639
    37 https://app.dimensions.ai/details/publication/pub.1075314221
    38 https://doi.org/10.1016/j.tig.2005.12.005
    39 https://doi.org/10.1016/s0140-6736(05)17947-1
    40 https://doi.org/10.1016/s1470-2045(07)70042-6
    41 https://doi.org/10.1056/nejmoa041588
    42 https://doi.org/10.1073/pnas.0506230102
    43 https://doi.org/10.1073/pnas.0701138104
    44 https://doi.org/10.1080/01621459.1958.10501452
    45 https://doi.org/10.1093/annonc/mdi352
    46 https://doi.org/10.1093/bioinformatics/btg405
    47 https://doi.org/10.1093/jnci/djj052
    48 https://doi.org/10.1093/nar/gkg763
    49 https://doi.org/10.1158/0008-5472.can-05-4414
    50 https://doi.org/10.1158/0008-5472.can-07-5206
    51 https://doi.org/10.1158/1078-0432.ccr-06-2765
    52 https://doi.org/10.1200/jco.2001.19.4.980
    53 https://doi.org/10.1200/jco.2005.04.7985
    54 https://doi.org/10.1200/jco.2005.06.178
    55 https://doi.org/10.1200/jco.2006.07.1522
    56 https://doi.org/10.1200/jco.2007.14.2364
    57 https://doi.org/10.1371/journal.pone.0005645
    58 https://doi.org/10.1677/erc-08-0338
    59 https://doi.org/10.3892/ijo.20.4.791
    60 schema:datePublished 2010-10
    61 schema:datePublishedReg 2010-10-01
    62 schema:description Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.
    63 schema:genre research_article
    64 schema:inLanguage en
    65 schema:isAccessibleForFree true
    66 schema:isPartOf Nad5d8f3e8c0043e59cfca7749030a754
    67 Nd10f40d7c16540cf9cb74cb6636c0226
    68 sg:journal.1092777
    69 schema:name An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients
    70 schema:pagination 725-731
    71 schema:productId N447aaeca41f24e7c8b979bf319ad21b7
    72 N8ed37207333044eb8bf935596cda3d08
    73 Nb80fc46b7cfa4e5bba93ac5c0888550c
    74 Nc1bb23a6ece5476687153252f353d871
    75 Ndde7826347b14bc99e868c80df0e24cf
    76 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019543499
    77 https://doi.org/10.1007/s10549-009-0674-9
    78 schema:sdDatePublished 2019-04-11T09:56
    79 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    80 schema:sdPublisher N5fbfe37ab0fc4d9ab0b980c20c1a6e61
    81 schema:url http://link.springer.com/10.1007%2Fs10549-009-0674-9
    82 sgo:license sg:explorer/license/
    83 sgo:sdDataset articles
    84 rdf:type schema:ScholarlyArticle
    85 N02d60add937146208e68a8cf597ed706 rdf:first sg:person.01015122154.36
    86 rdf:rest N59afd87df59f4135b5ee93f34d4c5321
    87 N0a25496d5cd643a3a7d5c47097a52bb4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    88 schema:name Lymphatic Metastasis
    89 rdf:type schema:DefinedTerm
    90 N0ec44ce507c04f5dbaca1262f727e9e7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    91 schema:name Reproducibility of Results
    92 rdf:type schema:DefinedTerm
    93 N10238f0aa19d44e1943a10a5380c6b67 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    94 schema:name Online Systems
    95 rdf:type schema:DefinedTerm
    96 N168a1cc871994e0ca4a7d0398b57e126 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    97 schema:name Genetic Markers
    98 rdf:type schema:DefinedTerm
    99 N18c770c4bfff40d49fe6c2ebbc803ca9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    100 schema:name Computer Graphics
    101 rdf:type schema:DefinedTerm
    102 N1d15a6f4531244ae8e0cf77d2979cc31 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    103 schema:name Middle Aged
    104 rdf:type schema:DefinedTerm
    105 N21aeca36fd764709b12e7e7dbbed728c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    106 schema:name Breast Neoplasms
    107 rdf:type schema:DefinedTerm
    108 N2336018e156b4e48b2fb9e18b7bba1f1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    109 schema:name Kaplan-Meier Estimate
    110 rdf:type schema:DefinedTerm
    111 N27e7baf5ad7c4e86afbaba1dc8ed313c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    112 schema:name Receptors, Estrogen
    113 rdf:type schema:DefinedTerm
    114 N3e39cd644a6d44eab9a3bc61106811be schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    115 schema:name Oligonucleotide Array Sequence Analysis
    116 rdf:type schema:DefinedTerm
    117 N447aaeca41f24e7c8b979bf319ad21b7 schema:name nlm_unique_id
    118 schema:value 8111104
    119 rdf:type schema:PropertyValue
    120 N561b890ec7ee4ca785aedb31c59cb7c3 rdf:first sg:person.0644146035.65
    121 rdf:rest N614af99fa9df44b0ae3127ebf472d776
    122 N59afd87df59f4135b5ee93f34d4c5321 rdf:first sg:person.01101666715.53
    123 rdf:rest N955074a5e66a496e92b10b9a9668c163
    124 N5cd73f9986fc4b3080cb04b091a48cd0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    125 schema:name Adult
    126 rdf:type schema:DefinedTerm
    127 N5fbfe37ab0fc4d9ab0b980c20c1a6e61 schema:name Springer Nature - SN SciGraph project
    128 rdf:type schema:Organization
    129 N614af99fa9df44b0ae3127ebf472d776 rdf:first sg:person.0777444624.40
    130 rdf:rest N7fa0e77a7d0f4aa8ac3928fc282e8a39
    131 N6895688fbdc947e7a29a3ea0208d579d rdf:first sg:person.0707551766.31
    132 rdf:rest N561b890ec7ee4ca785aedb31c59cb7c3
    133 N68d5cf1b11e045e9b9e2586f9a7cf53c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    134 schema:name Time Factors
    135 rdf:type schema:DefinedTerm
    136 N7fa0e77a7d0f4aa8ac3928fc282e8a39 rdf:first sg:person.01052576706.33
    137 rdf:rest N02d60add937146208e68a8cf597ed706
    138 N8ed37207333044eb8bf935596cda3d08 schema:name dimensions_id
    139 schema:value pub.1019543499
    140 rdf:type schema:PropertyValue
    141 N955074a5e66a496e92b10b9a9668c163 rdf:first sg:person.0767122747.55
    142 rdf:rest rdf:nil
    143 N97ad1278f25344fb88873154909c437c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    144 schema:name Female
    145 rdf:type schema:DefinedTerm
    146 N9bd45cc018b848ffb453ad9ae9584123 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    147 schema:name Neoplasm Staging
    148 rdf:type schema:DefinedTerm
    149 Na991ec0a0b1845ce96a3bccf52c89a28 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    150 schema:name Aged
    151 rdf:type schema:DefinedTerm
    152 Nad5d8f3e8c0043e59cfca7749030a754 schema:issueNumber 3
    153 rdf:type schema:PublicationIssue
    154 Nb272bfa7e6e94d65b79e5627e4bf890c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    155 schema:name Humans
    156 rdf:type schema:DefinedTerm
    157 Nb6eed43842b8465fb3b173bdb4f4f863 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    158 schema:name Gene Expression Profiling
    159 rdf:type schema:DefinedTerm
    160 Nb80fc46b7cfa4e5bba93ac5c0888550c schema:name doi
    161 schema:value 10.1007/s10549-009-0674-9
    162 rdf:type schema:PropertyValue
    163 Nbe13a18c8aba4f08ac461166f1592a1f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    164 schema:name Prognosis
    165 rdf:type schema:DefinedTerm
    166 Nc1bb23a6ece5476687153252f353d871 schema:name readcube_id
    167 schema:value 5d1e0e0c76f52a01f21cf1840edd199dc45dc433bc031b47f749def47d45502d
    168 rdf:type schema:PropertyValue
    169 Nc34b802bcfa04a13a40cb0f3fa74351a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    170 schema:name Biomarkers, Tumor
    171 rdf:type schema:DefinedTerm
    172 Nd10f40d7c16540cf9cb74cb6636c0226 schema:volumeNumber 123
    173 rdf:type schema:PublicationVolume
    174 Nd144e535aa394783a55b000b3c2833b2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    175 schema:name Disease-Free Survival
    176 rdf:type schema:DefinedTerm
    177 Ndde7826347b14bc99e868c80df0e24cf schema:name pubmed_id
    178 schema:value 20020197
    179 rdf:type schema:PropertyValue
    180 Ned15d6ca385f4a0c9328cd29ed67a3e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    181 schema:name Internet
    182 rdf:type schema:DefinedTerm
    183 Nfbd4d55ed3fd4bbe8d7f2d2580bd8615 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    184 schema:name Predictive Value of Tests
    185 rdf:type schema:DefinedTerm
    186 Nff29c95ba97a44e0921c8003626dcc15 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    187 schema:name Gene Expression Regulation, Neoplastic
    188 rdf:type schema:DefinedTerm
    189 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    190 schema:name Medical and Health Sciences
    191 rdf:type schema:DefinedTerm
    192 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
    193 schema:name Oncology and Carcinogenesis
    194 rdf:type schema:DefinedTerm
    195 sg:journal.1092777 schema:issn 0167-6806
    196 1573-7217
    197 schema:name Breast Cancer Research and Treatment
    198 rdf:type schema:Periodical
    199 sg:person.01015122154.36 schema:affiliation https://www.grid.ac/institutes/grid.6363.0
    200 schema:familyName Budczies
    201 schema:givenName Jan
    202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01015122154.36
    203 rdf:type schema:Person
    204 sg:person.01052576706.33 schema:affiliation https://www.grid.ac/institutes/grid.6363.0
    205 schema:familyName Denkert
    206 schema:givenName Carsten
    207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052576706.33
    208 rdf:type schema:Person
    209 sg:person.01101666715.53 schema:affiliation https://www.grid.ac/institutes/grid.5170.3
    210 schema:familyName Li
    211 schema:givenName Qiyuan
    212 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101666715.53
    213 rdf:type schema:Person
    214 sg:person.0644146035.65 schema:affiliation https://www.grid.ac/institutes/grid.5591.8
    215 schema:familyName Lanczky
    216 schema:givenName Andras
    217 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644146035.65
    218 rdf:type schema:Person
    219 sg:person.0707551766.31 schema:affiliation https://www.grid.ac/institutes/grid.11804.3c
    220 schema:familyName Györffy
    221 schema:givenName Balazs
    222 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707551766.31
    223 rdf:type schema:Person
    224 sg:person.0767122747.55 schema:affiliation https://www.grid.ac/institutes/grid.38142.3c
    225 schema:familyName Szallasi
    226 schema:givenName Zoltan
    227 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0767122747.55
    228 rdf:type schema:Person
    229 sg:person.0777444624.40 schema:affiliation https://www.grid.ac/institutes/grid.5170.3
    230 schema:familyName Eklund
    231 schema:givenName Aron C.
    232 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777444624.40
    233 rdf:type schema:Person
    234 sg:pub.10.1007/s10549-008-0183-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038471868
    235 https://doi.org/10.1007/s10549-008-0183-2
    236 rdf:type schema:CreativeWork
    237 sg:pub.10.1007/s10549-008-0242-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002015079
    238 https://doi.org/10.1007/s10549-008-0242-8
    239 rdf:type schema:CreativeWork
    240 sg:pub.10.1023/a:1017946810277 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035809985
    241 https://doi.org/10.1023/a:1017946810277
    242 rdf:type schema:CreativeWork
    243 sg:pub.10.1038/nature08021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010946448
    244 https://doi.org/10.1038/nature08021
    245 rdf:type schema:CreativeWork
    246 sg:pub.10.1038/nbt1239 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037875102
    247 https://doi.org/10.1038/nbt1239
    248 rdf:type schema:CreativeWork
    249 sg:pub.10.1186/1471-2164-9-239 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010596045
    250 https://doi.org/10.1186/1471-2164-9-239
    251 rdf:type schema:CreativeWork
    252 sg:pub.10.1186/1755-8794-1-42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034319319
    253 https://doi.org/10.1186/1755-8794-1-42
    254 rdf:type schema:CreativeWork
    255 sg:pub.10.1186/1755-8794-2-40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025616151
    256 https://doi.org/10.1186/1755-8794-2-40
    257 rdf:type schema:CreativeWork
    258 sg:pub.10.1186/bcr1325 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023450491
    259 https://doi.org/10.1186/bcr1325
    260 rdf:type schema:CreativeWork
    261 sg:pub.10.1186/bcr1639 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044880794
    262 https://doi.org/10.1186/bcr1639
    263 rdf:type schema:CreativeWork
    264 https://app.dimensions.ai/details/publication/pub.1075314221 schema:CreativeWork
    265 https://doi.org/10.1016/j.tig.2005.12.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044109724
    266 rdf:type schema:CreativeWork
    267 https://doi.org/10.1016/s0140-6736(05)17947-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047788005
    268 rdf:type schema:CreativeWork
    269 https://doi.org/10.1016/s1470-2045(07)70042-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027417986
    270 rdf:type schema:CreativeWork
    271 https://doi.org/10.1056/nejmoa041588 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022156409
    272 rdf:type schema:CreativeWork
    273 https://doi.org/10.1073/pnas.0506230102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002515049
    274 rdf:type schema:CreativeWork
    275 https://doi.org/10.1073/pnas.0701138104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028426046
    276 rdf:type schema:CreativeWork
    277 https://doi.org/10.1080/01621459.1958.10501452 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058299418
    278 rdf:type schema:CreativeWork
    279 https://doi.org/10.1093/annonc/mdi352 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015874660
    280 rdf:type schema:CreativeWork
    281 https://doi.org/10.1093/bioinformatics/btg405 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003878496
    282 rdf:type schema:CreativeWork
    283 https://doi.org/10.1093/jnci/djj052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030644591
    284 rdf:type schema:CreativeWork
    285 https://doi.org/10.1093/nar/gkg763 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019401433
    286 rdf:type schema:CreativeWork
    287 https://doi.org/10.1158/0008-5472.can-05-4414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008978686
    288 rdf:type schema:CreativeWork
    289 https://doi.org/10.1158/0008-5472.can-07-5206 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017296025
    290 rdf:type schema:CreativeWork
    291 https://doi.org/10.1158/1078-0432.ccr-06-2765 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030779547
    292 rdf:type schema:CreativeWork
    293 https://doi.org/10.1200/jco.2001.19.4.980 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074754816
    294 rdf:type schema:CreativeWork
    295 https://doi.org/10.1200/jco.2005.04.7985 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011750427
    296 rdf:type schema:CreativeWork
    297 https://doi.org/10.1200/jco.2005.06.178 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001535254
    298 rdf:type schema:CreativeWork
    299 https://doi.org/10.1200/jco.2006.07.1522 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003206258
    300 rdf:type schema:CreativeWork
    301 https://doi.org/10.1200/jco.2007.14.2364 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049142592
    302 rdf:type schema:CreativeWork
    303 https://doi.org/10.1371/journal.pone.0005645 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033676042
    304 rdf:type schema:CreativeWork
    305 https://doi.org/10.1677/erc-08-0338 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038686358
    306 rdf:type schema:CreativeWork
    307 https://doi.org/10.3892/ijo.20.4.791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071512157
    308 rdf:type schema:CreativeWork
    309 https://www.grid.ac/institutes/grid.11804.3c schema:alternateName Semmelweis University
    310 schema:name Joint Research Laboratory of the Hungarian Academy of Sciences and the Semmelweis University, Semmelweis University 1st Department of Pediatrics, Bokay u. 53-54, 1083, Budapest, Hungary
    311 rdf:type schema:Organization
    312 https://www.grid.ac/institutes/grid.38142.3c schema:alternateName Harvard University
    313 schema:name Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
    314 Children’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, MA, USA
    315 rdf:type schema:Organization
    316 https://www.grid.ac/institutes/grid.5170.3 schema:alternateName Technical University of Denmark
    317 schema:name Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
    318 rdf:type schema:Organization
    319 https://www.grid.ac/institutes/grid.5591.8 schema:alternateName Eötvös Loránd University
    320 schema:name Joint Research Laboratory of the Hungarian Academy of Sciences and the Semmelweis University, Semmelweis University 1st Department of Pediatrics, Bokay u. 53-54, 1083, Budapest, Hungary
    321 Pazmany Peter University, Budapest, Hungary
    322 rdf:type schema:Organization
    323 https://www.grid.ac/institutes/grid.6363.0 schema:alternateName Charité
    324 schema:name Charité Universitaetsmedizin, Berlin, Germany
    325 rdf:type schema:Organization
     




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


    ...