Data clustering in life sciences.
Clustering has a wide range of applications in life sciences and over the years has been used in many areas ranging from the analysis of clinical information, phylogeny, genomics, and proteomics. The primary goal of this article is to provide an overview of the various issues involved in clustering large biological datasets, describe the merits and underlying assumptions of some of the commonly used clustering approaches, and provide insights on how to cluster datasets arising in various areas within life sciences. We also provide a brief introduction to CLUTO, a general purpose toolkit for clustering various datasets, with an emphasis on its applications to problems and analysis requirements within life sciences.
Zhao
Ying
Y
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Karypis
George
G
D000465:Algorithms; D001690:Biological Science Disciplines; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D020411:Oligonucleotide Array Sequence Analysis