Microarray gene expression data are produced around the world in huge amounts. This data is not easy to analyse manually. Therefore, there is a need for automated techniques to do the job. Clustering is one of the automated techniques that can be handed to a computer to cluster the data to groups depending on properties that this data share. An important source of information for such automated techniques is the data about genes in large shared text databases such as Swiss-Prot. This book explores and evaluates MAXCCLUS, a bioinformatics clustering algorithm, which clusters genes from microarray experimental data. MAXCCLUS does the clustering of genes depending on the textual data that describe the genes. It attempts to create clusters of which it selects only the statistically significant ones by running a significance test. It then attempts to generalise these clusters by using a simple greedy generalisation algorithm. We explore the behaviour of MAXCCLUS by running several clustering experiments that investigate various modifications to MAXCCLUS and its data. This book should be useful to researchers and every one interested in clustering of microarray gene expression data.
Exploring a Bioinformatics Clustering Algorithm - Methodology, Experiments, Analysis
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Description
Microarray gene expression data are produced around the world in huge amounts. This data is not easy to analyse manually. Therefore, there is a need for automated techniques to do the job. Clustering is one of the automated techniques that can be handed to a computer to cluster the data to groups depending on properties that this data share. An important source of information for such automated techniques is the data about genes in large shared text databases such as Swiss-Prot. This book explores and evaluates MAXCCLUS, a bioinformatics clustering algorithm, which clusters genes from microarray experimental data. MAXCCLUS does the clustering of genes depending on the textual data that describe the genes. It attempts to create clusters of which it selects only the statistically significant ones by running a significance test. It then attempts to generalise these clusters by using a simple greedy generalisation algorithm. We explore the behaviour of MAXCCLUS by running several clustering experiments that investigate various modifications to MAXCCLUS and its data. This book should be useful to researchers and every one interested in clustering of microarray gene expression data.
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