Clustering Algorithms: Their Application to Gene Expression Data

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the chall...

Effective Clustering Algorithms for Gene Expression Data

Clustering Algorithms for Time Series Gene Expression in Microarray Data

illustrations, 75 numbered references. Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the ...

Consensus clustering of gene expression microarray data using genetic algorithms

This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets – DA and DB – as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The g...

Performance Analysis of Clustering Algorithms for Gene Expression Data

Microarray technology is a process that allows thousands of genes simultaneously monitor to various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins, This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be...