Robust Clustering for Gene Expression Data Study in Bioinformatics
Statistical Robust Clustering Approach for Gene Expression Microarry Data Analysis
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DNA microarray technology has now possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of different types of measurements. An important step toward addressing this challenge is the use of robust clustering techniques, which is essential in the data mining process to reveal natural structures and iden...
DNA microarray technology has now possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of different types of measurements. An important step toward addressing this challenge is the use of robust clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data in Bioinformatics.This book greatly attracts a broad range of scientists who are interested in DNA microarray data analysis, because it provides a practical method critically necessary for gene expression data clustering and discovery of some genes responsible for cancer disease. Currently, breast cancer is the most common type of cancer and often causes death among women in the world. The presentation of this book is easy and intelligible by the beginner and help who are undertaking researcher on gene expression data analysis in Bioinformatics
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