GenClust: A genetic algorithm for clustering gene expression data

Background: Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results: GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update; (b) it can be used naturally in conjunction with data driven internal validation methods. We have experimented with the FOM methodology, specifically conceived for validating clusters of gene expression data. The validity of GenClust has been assessed experimentally on real data...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Vito Di Gesù, Raffaele Giancarlo, Giosuè Lo Bosco, Alessandra Raimondi, Davide Scaturro
• Keywords: Algorithms, BMCBI 2005, Gene Expression Data, Validation Measures