Metacore – Clarivate
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Follow a présentation of Metacore in english:
« MetaCore » is a data analysis software of the « omics » family (genomics, proteomics, transcriptomics) that allows a better understanding of the biological mechanisms linked to these data due to data curation and the integration into our teams of all the interactions that have been scientifically validated by your peers. This platform contains more than 1.5 million molecular interactions, more than 1.5 thousand pathway maps and 10 construction algorithms of interaction networks. The MetaCore platform has been evaluated by a group of researchers from the New York School of Medicine and was classed first in terms of precision, reliability and exhaustion*. Manual curation is carried out by holders of PhD and MD diplomas, with an update every three months. MetaCore is used by the centres of public research such as Harvard University, Biological Research Centres in Oxford and Cambridge, the Karolinska Institute or Cancer Research UK.
The main functions allow:
Pathway enrichment starting from your data due to the manual curation by our editorial team;
Comparisons of experimental data to be made (for example Sick vs Control)to demonstrate differences in in terms of biological mechanisms;
Analysis of interaction networks (identification of key regulators;
The Key Pathway Advisor is a complementary module of MetaCore, which allows researchers to understand the regulatory cascades that explain the molecular mechanisms demonstrated in MetaCore. The main functions allow:
Upstream to link the genetic expression data with the preliminary molecular regulatory cascades and thus predict the Key Hubs (transcription factor/enzyme/receptor) which are at the origin; qui en sont à l’origine;
Downstream to identify the pathways affected by these expression data;
To identify new biomarkers as well as therapeutic molecules that are candidates to influence these regulation networks »
*Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale Evgeny Shmelkov1,2, Zuojian Tang2, Iannis Aifantis3, Alexander Statnikov2,4*