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BMCBIOINFORMATICS . BIOMEDCENTRAL . COM {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Bmcbioinformatics.biomedcentral.com Make Money
  6. How Much Does Bmcbioinformatics.biomedcentral.com Make
  7. Keywords
  8. Topics
  9. Questions
  10. Schema
  11. Social Networks
  12. External Links
  13. Analytics And Tracking
  14. Libraries

We are analyzing https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-s16-s13.

Title:
Knowledge-based analysis of proteomics data | BMC Bioinformatics | Full Text
Description:
As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.
Website Age:
25 years and 10 months (reg. 1999-08-06).

Matching Content Categories {πŸ“š}

  • Science
  • Education
  • Cryptocurrency

Content Management System {πŸ“}

What CMS is bmcbioinformatics.biomedcentral.com built with?

Custom-built

No common CMS systems were detected on Bmcbioinformatics.biomedcentral.com, and no known web development framework was identified.

Traffic Estimate {πŸ“ˆ}

What is the average monthly size of bmcbioinformatics.biomedcentral.com audience?

πŸš€ Good Traffic: 50k - 100k visitors per month


Based on our best estimate, this website will receive around 50,019 visitors per month in the current month.
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How Does Bmcbioinformatics.biomedcentral.com Make Money? {πŸ’Έ}


Display Ads {🎯}


The website utilizes display ads within its content to generate revenue. Check the next section for further revenue estimates.

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Direct Advertisers (3)
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How Much Does Bmcbioinformatics.biomedcentral.com Make? {πŸ’°}


Display Ads {🎯}

$690 per month
Our estimates place Bmcbioinformatics.biomedcentral.com's monthly online earnings from display ads at $462 to $1,271.

Keywords {πŸ”}

proteins, network, nodes, analysis, protein, list, interactions, proteomics, set, ontology, enrichment, node, number, data, pathways, networks, gene, genes, algorithm, interactome, functional, size, shortest, human, objects, interest, cancer, dataset, paths, processes, connectivity, pathway, figure, expression, interaction, significant, pubmed, article, terms, metacore, transcription, profiles, statistical, disease, based, input, canonical, google, scholar, ontologies,

Topics {βœ’οΈ}

𝐾 𝑖 𝑗 organ/tissue/body liquid/cell line shotgun lc-ms/ms analysis open access article genome-wide functional synergy tatiana nikolskayaΒ &Β yuri nikolsky /bmcbioinformatics/supplements/13/s16 proprietary java-based editor post-translational level leaving full size image bmc medical genomics bmc systems biology multi-variant proteomics profiles metacore/metadrug analysis platform authors scientific editing 𝑁 - 𝑅 privacy choices/manage cookies knowledge-based approach deals google scholar 𝑁 - 𝑛 full size table human cancer cells sufficient hypothesis-generation tool custom gene/protein sets prostate cancer patients organ-specific histopathology prostate cancer datasets bmc bioinformatics bmc bioinformatics 13 tissue-specific biological context multi-step connectivity combinations prostate cancer biomarkers early ovarian tumorigenesis extracellular matrix remodelling high clustering coefficient red boxes-proteins identified text mining algorithms ovarian cancer study super-connected large network identifying potential biomarkers complement pathway activation human protein-encoding genes signal transduction interactions protein-based network objects breast cancer pooled mechanistic networks linking shortest path network p-values shows relevance multi-step modules teal blue lines

Questions {❓}

  • Ontology enrichment cannot rank individual proteins based on significance and answer questions such as "what is the most important protein kinase for my dataset?
  • Rivals I, Personnaz L, Taing L, Potier M-C: Enrichment or depletion of a GO category within a class of genes: which test?
  • What is the value and relevance of this "accumulated knowledge" for the analysis of an individual proteomic profile and how could it be applied in meaningful way?

Schema {πŸ—ΊοΈ}

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      description:As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.
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Social Networks {πŸ‘}(2)

External Links {πŸ”—}(100)

Analytics and Tracking {πŸ“Š}

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Libraries {πŸ“š}

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