oposSOM

LHA-ID
7Q0CTG2MJJ-6
Motivation

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.

Description

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

Publication Date
Sponsor
BMBF (Federal Ministry of Education and Research)
Helmholtz Association
ESF (European Social Fund for Germany)
Contact
Dr. Henry Löffler-Wirth
Contact Institution

IZBI Härtelstr. 16-18 04107 Leipzig

Version Number
1.12.0
Development Environment
R