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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.


1 item is associated with this Model:

Human Disease: Not specified

Model type: Not specified

Model format: R package

Execution or visualisation environment: RStudio

Model image: No image specified

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Created: 6th May 2019 at 12:36

Last updated: 15th May 2019 at 12:56

Last used: 25th Jun 2019 at 17:35

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Leipzig Health Atlas

The Leipzig Health Atlas is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

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