Journal
BIOINFORMATICS
Volume 31, Issue 19, Pages 3225-3227Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv342
Keywords
-
Categories
Funding
- Federal Ministry of Education and Research (BMBF) [FKZ 031 6166, FKZ 031 6065A]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available