Validating clustering for gene expression data

Validating clustering for gene expression data

MOTIVATION Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms. Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. Our methodology is to app...

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...

Effective Clustering Algorithms for Gene Expression Data

Efficient Clustering for Gene Expression Data

In the past decade there have been advance in technologies, the amount of biological data such as DNA sequences and microarray data have been increased tremendously. To obtain knowledge from the data, explore relationships between genes, understanding severe diseases and development of drugs for patterns from the databases of large size and high dimensionality. Information retrieval and data mi...

Hybrid Algorithm for Clustering Gene Expression Data

Microarray gene expressions provide an insight into genomic biomarkers that aid in identifying cancerous cells and normal cells. In this study, functionally related genes are identified by partitioning gene data. Clustering is an unsupervised learning technique that partition gene data into groups based on the similarity between their expression profiles. This identifies functionally related ge...