The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance

K. Vengatesan, S. B. Mahajan, P. Sanjeevikumar, Sana Moin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Systems, Control and Automation - ETAEERE-2016
PublisherSpringer Verlag
Pages671-678
Number of pages8
ISBN (Print)9789811047619
DOIs
Publication statusPublished - 01-01-2018
Externally publishedYes
EventInternational Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016 - Majhitar, India
Duration: 17-12-201618-12-2016

Publication series

NameLecture Notes in Electrical Engineering
Volume442
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016
CountryIndia
CityMajhitar
Period17-12-1618-12-16

Fingerprint

Analysis of variance (ANOVA)
Genes
Gene expression
Statistical methods
Agglomeration
Testing

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Vengatesan, K., Mahajan, S. B., Sanjeevikumar, P., & Moin, S. (2018). The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance. In Advances in Systems, Control and Automation - ETAEERE-2016 (pp. 671-678). (Lecture Notes in Electrical Engineering; Vol. 442). Springer Verlag. https://doi.org/10.1007/978-981-10-4762-6_64
Vengatesan, K. ; Mahajan, S. B. ; Sanjeevikumar, P. ; Moin, Sana. / The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance. Advances in Systems, Control and Automation - ETAEERE-2016. Springer Verlag, 2018. pp. 671-678 (Lecture Notes in Electrical Engineering).
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Vengatesan, K, Mahajan, SB, Sanjeevikumar, P & Moin, S 2018, The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance. in Advances in Systems, Control and Automation - ETAEERE-2016. Lecture Notes in Electrical Engineering, vol. 442, Springer Verlag, pp. 671-678, International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016, Majhitar, India, 17-12-16. https://doi.org/10.1007/978-981-10-4762-6_64

The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance. / Vengatesan, K.; Mahajan, S. B.; Sanjeevikumar, P.; Moin, Sana.

Advances in Systems, Control and Automation - ETAEERE-2016. Springer Verlag, 2018. p. 671-678 (Lecture Notes in Electrical Engineering; Vol. 442).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Vengatesan K, Mahajan SB, Sanjeevikumar P, Moin S. The Performance Enhancement of Statistically Significant Bicluster Using Analysis of Variance. In Advances in Systems, Control and Automation - ETAEERE-2016. Springer Verlag. 2018. p. 671-678. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-10-4762-6_64