期刊
BIOINFORMATICS
卷 31, 期 19, 页码 3225-3227出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv342
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资金
- Federal Ministry of Education and Research (BMBF) [FKZ 031 6166, FKZ 031 6065A]
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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