Methods for supervised and unsupervised clustering and machine learning were studied in order to automatically model relationships between gene expression data and gene functions of the microorganism Escherichia coli. From a pre-selected subset of 265 genes (belonging to 3 functional groups) the function has been predicted with an accuracy of 63-71 % by various data mining methods described in ...

The recent advances of array technologies have made it possible to monitor huge amount of genes expression data. Clustering, for example, hierarchical clustering, self-organizing maps (SOM), kmeans clustering, has become important analysis for such gene expression data. We have applied the Fuzzy adaptive resonance theory (Fuzzy ART) [5] to the gene clustering of DNA microarray data and the clus...

MOTIVATION Cluster analysis (of gene-expression data) is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Various methods have been proposed for clustering gene-expression data. However most of these algorithms have several shortcomings for gene-expression data clustering. In the present article, we...

Data clustering techniques have been applied to extract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success. However, due to the diverse structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, we aim to the feature of high no...

Reverse engineering of genetic regulatory network involves the modeling of the given gene expression data into a form of the network. Computationally it is possible to have the relationships between genes, so called gene regulatory networks (GRNs), that can help to find the genomics and proteomics based diagnostic approach for any disease. In this paper, clustering based method has been used to...

Systems biology comprises the global, integrated analysis of large-scale data encoding different levels of biological information with the aim to obtain global insight into the cellular networks. Several studies have unveiled the modular and hierarchical organization inherent in these networks. In this dissertation, we propose and develop innovative systems approaches to integrate multi-source ...

Clustering is a very useful and important technique for analyzing gene expression data. Self-organizing map (SOM) is one of the most useful clustering algorithms. SOM requires the number of clusters to be one of the initialization parameters prior to clustering. However, this information is unavailable in most cases, particularly in gene expression data. Thus, the validation results from SOM ar...

we can reach by dna microarray gene expression to such wealth of information with thousands of variables (genes). analysis of this information can show genetic reasons of disease and tumor differences. in this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...

-The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Biclustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. In recent years, several biclustering methods have been suggested to identify local patterns in gene expression data. Most of these algorithms represent gr...