oposSOM
Version 2

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.

LHA ID: 7Q0CTG2MJJ-6

1 item is associated with this Model:

Human Disease: Not specified

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Model format: R package

Execution or visualisation environment: Shiny




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Views: 2555

Created: 6th May 2019 at 12:36

Last updated: 15th May 2019 at 12:56

Last used: 23rd May 2022 at 10:38

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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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