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Analysis of large-scale molecular biological data using self-organizing maps

Comprehensive analysis of genome-wide molecular data challenges bioinformatics methodology in terms of intuitive visualization with single-sample resolution, biomarker selection, functional information mining and highly granular stratification of sample classes. oposSOM combines those functionalities making use of a comprehensive analysis and visualization strategy based on self-organizing maps (SOM) machine learning which we call 'high-dimensional data portraying'. The method was successfully applied in a series of studies using mostly transcriptome data but also data of other OMICs realms.


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Views: 4290

Created: 6th May 2019 at 12:36

Last updated: 15th May 2019 at 12:56

Last used: 20th May 2024 at 12:41

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Version 2 (latest) Created 6th May 2019 at 12:37 by Henry Löffler-Wirth

replaced static file with link to Bioconductor

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