Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene express...

Different clustering techniques such as Self-Organizing Map (SOM), and hierarchical clustering, among others, have been applied to gene expression data. The focuses of theses studies are often on the biological results, and there is no indication on what methods are more suitable for clustering gene expression. In this paper, an evaluation methodology that assesses the stability of clustering m...

Clustering algorithms have been successfully applied to identify co-expressed gene groups from expression data. Missing values often occur in data, which presents a challenge for clustering. When partitioning incomplete data into groups, missing value imputation and clustering are generally performed as two separate processes. These two-stage methods likely result unsuitable task unsatisfying p...

With the rapid advances of microarray technologies, large amounts of high-dimensional gene expression data are being generated, which poses significant computational challenges. A first step towards addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. A ...

Recently, gene expression data under various conditions have largely been obtained by the utilization of the DNA microarrays and oligonucleotide arrays. There have been emerging demands to analyze the function of genes from the gene expression profiles. For clustering genes from their expression profiles, hierarchical clustering has been widely used. The clustering method represents the relatio...

Microarray technology helps biologists for monitoring expression of thousands of genes in a single experiment on a small chip. Microarray is also called as DNA chip, gene chip, or biochip is used to analyze the gene expression profiles. After genome sequencing, DNA microarray analysis has become the most widely used functional genomics approach in the bioinformatics field. Biologists are vastly...

background: gliomas are the most frequently observed primary brain tumors. these tumors comprise a variety of different histological tumor types and malignancy grades. oligodendrogliomas typically contain a rich network of branching capillaries. approximately 50%-80% of oligodendrogliomas demonstrate a combined loss of chromosomes 1p and 19q. oligodendrogliomas differ from neurocytomas in that ...

Microarrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems, including classification of disease subtypes and tumors. Many statistical methods are available for analyzing and systematizing these complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or ...

Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (numbe...

Recent advances in high throughput profiling of gene expression have catalyzed an explosive growth in functional genomics aimed at the elucidation of genes that are differentially expressed in various tissue or cell types across a range of experimental conditions. These studies can lead to the identification of diagnostic genes, classification of genes into functional categories, association of...