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Genes Analysis of Data by Using Hierarchical Quality Threshold Clustering

Shiv Kumar, Vijay K. Chaudhari, Md. Ilyas Khan, Neetesh Gupta, Bupendra Verma


In this paper “Genes Analysis of Data by Using Hierarchical Quality Threshold Clustering” is an approach which proposed dynamically Growing Hierarchical Self Organizing Map (DGHSOM) with Nano array to identify co-expressed genes. The DGHSOM overcomes the problem of specifying the number of clusters and total number of iteration before the processing now, we are using QT (quality threshold) clustering is a method of partitioning data, which is invented for gene clustering. It requires more computing power than k- means, but does not require specifying the number of clusters. DNA Nano array technology is a challenging area in bioinformatics research, as we have to monitor millions of genes simultaneously. The expression profile of the gene can be useful in cancer disease analysis and its diagnosis. Gene expression data is very voluminous and very difficult to analyze. Several clustering algorithm have been proposed to identify co expressed genes. The Self-organizing-maps (SOM) is a powerful tool for recognizing and classifying features in complex, micro array data. But the interpretation of co- expression of genes are heavily depends on domain knowledge and SOM lacks since the number of clusters must be determined before training.


Gene Expression Profile, Image Processing, Dynamically Growing Self Organizing Map, Nano Array, Qt Clustering

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