Abstract
In this article, the performance enhancement of statistically significant bicluster using analysis of variance is articulated. Various statistical methods are used to analyze the gene expression level. It is found that analysis of variance is one of the efficient methods for aggregation between a pair of genes. It computes the values by comparing the mean value of each group, and results are tested using the hypothesis to calculate the p-value. Various tests are conducted to increase the performance of the gene pair. Various clustering techniques are functional to investigate the gene expression information for both homogeneous and heterogeneous. Statistical approaches are used to identify the relevant information from the subset of genes. Various testing methods were conducted to enhance the performance of correlated genes. When compared with the biclustering methods such as the paired t-test, two-sample tests, the ANOVAs One sample test produces better result.
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Vengatesan, K., Mahajan, S.B., Sanjeevikumar, P., Moin, S. (2018). The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance. In: Konkani, A., Bera, R., Paul, S. (eds) Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol 442. Springer, Singapore. https://doi.org/10.1007/978-981-10-4762-6_64
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DOI: https://doi.org/10.1007/978-981-10-4762-6_64
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