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DOI . ORG {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Doi.org Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Schema
  10. Social Networks
  11. External Links
  12. Analytics And Tracking
  13. Libraries
  14. Hosting Providers
  15. CDN Services

We began analyzing https://www.nature.com/articles/s41467-019-13803-0, but it redirected us to https://www.nature.com/articles/s41467-019-13803-0. The analysis below is for the second page.

Title[redir]:
Interpreting pathways to discover cancer driver genes with Moonlight | Nature Communications
Description:
Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions. Identification of cancer driver genes, especially those that can act as tumour suppressors or oncogenes depending on context, remains a challenge. Here, the authors introduce Moonlight, a tool that integrates multi-omic data to address this challenge and identify numerous dual-role cancer genes.

Matching Content Categories {📚}

  • Science
  • Education
  • Health & Fitness

Content Management System {📝}

What CMS is doi.org built with?

Custom-built

No common CMS systems were detected on Doi.org, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of doi.org audience?

🏙️ Massive Traffic: 50M - 100M visitors per month


Based on our best estimate, this website will receive around 80,479,999 visitors per month in the current month.

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How Does Doi.org Make Money? {💸}

We find it hard to spot revenue streams.

While profit motivates many websites, others exist to inspire, entertain, or provide valuable resources. Websites have a variety of goals. And this might be one of them. Doi.org might have a hidden revenue stream, but it's not something we can detect.

Keywords {🔍}

cancer, genes, pubmed, data, moonlight, tumor, article, gene, google, scholar, cas, driver, cell, analysis, supplementary, central, expression, breast, oncogenes, fig, types, suppressors, methods, oncogene, chromatin, predicted, samples, identified, biological, mutation, dualrole, molecular, carcinoma, proliferation, information, oncogenic, mutations, suppressor, function, study, nature, mediators, apoptosis, approach, process, promoter, tcga, methylation, number, processes,

Topics {✒️}

gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations uk/pub/project/cancerrxgene/releases/current_release/sanger1018_brainarray_ensemblgene_rma uk/pub/project/cancerrxgene/releases/current_release/cell_lines_details nature portfolio supplementary information files privacy policy org/data/protocol/pancan-mutation-set {\epsilon }}s_i}y_{kj}} }}{{\sqrt additional multi-omics data /β-trcp/β-catenin pathways advertising n-wasp/fak/f-actin }}_{{\mathrm{kj}}}={\mathrm{ increased}} }}_{{\mathrm{kj}}}{\mathrm{ = affected}} }}_{{\mathrm{kj}}}={\mathrm{ decreased}} supplementary software 1 national research council social media nature 489 nature 483 nature io/ reprints incorporating additional data gdc data portal supplementary data 1 supplementary data 3 supplementary data 4 supplementary data 5 supplementary data 6 supplementary data 7 supplementary data 8 supplementary data 9 supplementary data 10 supplementary data 11 supplementary data 12 supplementary data 13 supplementary data 2 supplementary data 14 additional data integration single-cell -omics data data portal xi steven chen reporting summary k-nearest neighbor distances lettres research university folate-coupled enzyme mthfd2 atac-seq data ep300 identifies ep300-r1627w renal clear-cell carcinoma

Questions {❓}

  • GATA3 in breast cancer: tumor suppressor or oncogene?

Schema {🗺️}

WebPage:
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         headline:Interpreting pathways to discover cancer driver genes with Moonlight
         description:Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions. Identification of cancer driver genes, especially those that can act as tumour suppressors or oncogenes depending on context, remains a challenge. Here, the authors introduce Moonlight, a tool that integrates multi-omic data to address this challenge and identify numerous dual-role cancer genes.
         datePublished:2020-01-03T00:00:00Z
         dateModified:2020-01-03T00:00:00Z
         pageStart:1
         pageEnd:17
         license:http://creativecommons.org/licenses/by/4.0/
         sameAs:https://doi.org/10.1038/s41467-019-13803-0
         keywords:
            Cancer genomics
            Oncogenes
            Software
            Science
            Humanities and Social Sciences
            multidisciplinary
         image:
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ScholarlyArticle:
      headline:Interpreting pathways to discover cancer driver genes with Moonlight
      description:Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions. Identification of cancer driver genes, especially those that can act as tumour suppressors or oncogenes depending on context, remains a challenge. Here, the authors introduce Moonlight, a tool that integrates multi-omic data to address this challenge and identify numerous dual-role cancer genes.
      datePublished:2020-01-03T00:00:00Z
      dateModified:2020-01-03T00:00:00Z
      pageStart:1
      pageEnd:17
      license:http://creativecommons.org/licenses/by/4.0/
      sameAs:https://doi.org/10.1038/s41467-019-13803-0
      keywords:
         Cancer genomics
         Oncogenes
         Software
         Science
         Humanities and Social Sciences
         multidisciplinary
      image:
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      isPartOf:
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            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Antonio Colaprico
            affiliation:
                  name:Interuniversity Institute of Bioinformatics in Brussels (IB)2
                  address:
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            name:Catharina Olsen
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            name:Thilde Terkelsen
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                  name:Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center
                  address:
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            name:Tiago C. Silva
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            name:André V. Olsen
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            name:Laura Cantini
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      name:Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
      name:Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), Milan, Italy
      name:Interuniversity Institute of Bioinformatics in Brussels (IB)2, Brussels, Belgium
      name:Machine Learning Group, Université Libre de Bruxelles (ULB), Brussels, Belgium
      name:Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, USA
      name:Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, USA
      name:Computational Biology Laboratory, and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center, Copenhagen, Denmark
      name:Translational Disease System Biology, Faculty of Health and Medical Science, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

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