Expression cartography of human tissues using self organizing maps.

ABSTRACT:

Background

Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.

Results

The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy

SUBMITTER: Wirth H 

PROVIDER: S-EPMC3161046 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature