Gene Expression Data Clustering

Techniques for clustering gene expression data

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognise these limitations a...

Algorithmic Approaches to Clustering Gene Expression Data

Technologies for generating high-density arrays of cDNAs and oligonucleotides are developing rapidly, and changing the landscape of biological and biomedical research. They enable, for the rst time, a global, simultaneous view on the transcription levels of many thousands of genes, when the cell undergoes speci c conditions or processes. For several organisms that had their genomes completely s...

Effective Clustering Algorithms for Gene Expression Data

Stable Clustering of Temporal Gene Expression Data

K-means and its many variants are widely used in bioinformatics. However, one of the drawbacks is that the clustering results are dependent of the initial choice of centroids, hence the experimental results are less reproducible. In addition, poor handling of nonspherical clusters is another weakness of the K-means algorithm. Thus, we investigate spectral clustering on temporal gene expression ...

Hierarchical Clustering of Gene Expression Data

Rapid development of biological technologies generates a hug amount of data, which provides a processing and global view of the gene expression levels across different conditions and over multiple stages. Analyzation and interpretation of these massive data is a challenging task. One of the most important steps is to extract useful and rational fundamental patterns of gene expression inherent i...