by Ariel E Bayá, Pablo M Granitto
Abstract:
The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.
Reference:
Clustering gene expression data with a penalized graph-based metric. (Ariel E Bayá, Pablo M Granitto), In BMC Bioinformatics, BioMed Central Ltd, volume 12, 2011.
Bibtex Entry:
@article{Baya2011,
abstract = {The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.},
author = {Bay\'{a}, Ariel E and Granitto, Pablo M},
doi = {10.1186/1471-2105-12-2},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Algorithms,Cluster Analysis,Gene Expression Profiling,Gene Expression Profiling: methods,SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = jan,
number = {1},
pages = {2},
pmid = {21205299},
publisher = {BioMed Central Ltd},
title = {{Clustering gene expression data with a penalized graph-based metric.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3023695\&tool=pmcentrez\&rendertype=abstract},
volume = {12},
year = {2011}
}