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© Research
Publication : Bioinformatics (Oxford, England)

Approximate geodesic distances reveal biologically relevant structures in microarray data

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Bioinformatics (Oxford, England) - 29 Jan 2004

Nilsson J, Fioretos T, Höglund M, Fontes M

Link to Pubmed [PMID] – 14752004

Bioinformatics 2004 Apr;20(6):874-80

Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance.

http://www.ncbi.nlm.nih.gov/pubmed/14752004