DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Currently most research focuses on the interpretation of the meaning of the data. However, majority methods are supervised-based, less attention has been paid on unsupervised approaches which is important when domain knowledge is incomplete or hard to obtain. In this paper, we present a new framework f...

The study on gene expression profiling of tissues and cells has become a major tool for discovery in medicine. Identification of co-expressed genes and coherent patterns is the central goal in gene expression profiling and the important task in the field of bioinformatics research. Clustering is an important unsupervised learning technique for Gene Expression Profile Analysis. Many conventional...

Various clustering methods have been applied to microarray gene expression data to identify genes with similar expression profiles. Recently, as the biological annotation data accumulated, many genes have been organized into functional categories such as Gene Ontology. Because functionally related genes may be regulated by common cellular signals, and hence be co-expressed, how to utilize these...

We have proposed an ant-based clustering algorithm for document clustering based on the travelling salesperson scenario. In this paper, we presented an approach called Ant-MST for gene expression data clustering based on both ant-based clustering and minimum spanning trees (MST). The ant-based clustering algorithm is firstly used to construct a fully connected network of nodes. Each node repres...

We present a novel approach to the clustering of gene expression patterns based on the mutual connectivity of the patterns. Unlike certain widely used methods (e.g., self-organizing maps and K-means) which essentially force gene expression data into a xed number of predetermined clustering structures, our approach aims to reveal the natural tendency of the data to cluster, in analogy to the phy...

Clustering is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes (dimensions). The k-means and hierarchical as well as self-organizing maps have all been used for clustering expression profiles and a number of algorithms have been developed for expression data and applied to analyze it. These Clustering methods usually use metric dist...

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First...

The increasing use of microarray technologies is generating a large amount of data that must be processed to extract underlying gene expression patterns. Existing clustering methods could suffer from certain drawbacks. Most methods cannot automatically separate scattered, singleton and mini-cluster genes from other genes. Inclusion of these types of genes into regular clustering processes can i...

conclusions some results of the correlation coefficients are not the same with visualization. the reason may be due to the small number of data. materials and methods in the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as spearman’s rank correlation coefficient and blomqvist’s measure, and compared them with pearson’s correlation coefficie...

There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to ana...