Gene expression data analysis has become an important topic in bioinformatics due to its wide application in the biomedical industry. Effective analysis of gene expression data is an essential part of various data mining methods, especially the clustering techniques. Various kinds of clustering methods have been proposed, yet they do not satisfy for the requirements of high efficiency, high qua...

Huge amount of gene expression data have been generated as a result of the human genomic project. Clustering has been used extensively in mining these gene expression data to find important genetic and biological information. Obtaining high quality clustering results is very challenging because of the inconsistency of the results of different clustering algorithms and noise in the gene expressi...

the aim of this study was to propose a method for improving the power of recognition and classification of thromboembolic syndrome based on the analysis of ‎ gene expression data using artificial neural networks. the studied method was performed on a dataset which contained data about 117 patients admitted to a hospital in durham in 2009. of all the studied patients, 66 patients were suffering ...

Eisen’s tree view is a useful tool for clustering and displaying of microarray gene expression data. In Eisen’s tree view system, a hierarchical method is used for clustering data. However, some useful information in gene expression data may not be well drawn when a hierarchical clustering is directly used in Eisen’s tree view. In this paper, we embed the similarity-based clustering method (SCM...

Extracting the information with biological significance in amounts of gene expression data is an important research direction. Clustering algorithm in this area has been increasingly widely applied. According to the characteristic of gene expression data, the improved projection pursuit cluster model was introduced in this area and Quantum-behaved Particle Swarm Optimization(QPSO) was put forwa...