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Sethi P, Alagiriswamy S. Association rule based similarity measures for the clustering of gene expression data. Open Med Inform J. 2010;4:63-73doi: 10.2174/1874431101004010063.
Sethi, P., & Alagiriswamy, S. (2010). Association rule based similarity measures for the clustering of gene expression data. The open medical informatics journal, 463-73. https://doi.org/10.2174/1874431101004010063
Sethi, Prerna, and Alagiriswamy, Sathya. "Association rule based similarity measures for the clustering of gene expression data." The open medical informatics journal vol. 4 (2010): 63-73. doi: https://doi.org/10.2174/1874431101004010063
Sethi P, Alagiriswamy S. Association rule based similarity measures for the clustering of gene expression data. Open Med Inform J. 2010;4:63-73. doi: 10.2174/1874431101004010063. Epub 2010 May 28. PMID: 21603179; PMCID: PMC3096052.
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Open Med Inform J. 2010;4:63-73. doi: 10.2174/1874431101004010063. Epub 2010 May 28.

Association rule based similarity measures for the clustering of gene expression data.

The open medical informatics journal

Prerna Sethi, Sathya Alagiriswamy

Affiliations

  1. Department of Health Informatics and Information Management and Biological Sciences, Ruston, USA. [email protected]

PMID: 21603179 PMCID: PMC3096052 DOI: 10.2174/1874431101004010063

Abstract

In life threatening diseases, such as cancer, where the effective diagnosis includes annotation, early detection, distinction, and prediction, data mining and statistical approaches offer the promise for precise, accurate, and functionally robust analysis of gene expression data. The computational extraction of derived patterns from microarray gene expression is a non-trivial task that involves sophisticated algorithm design and analysis for specific domain discovery. In this paper, we have proposed a formal approach for feature extraction by first applying feature selection heuristics based on the statistical impurity measures, the Gini Index, Max Minority, and the Twoing Rule and obtaining the top 100-400 genes. We then analyze the associative dependencies between the genes and assign weights to the genes based on their degree of participation in the rules. Consequently, we present a weighted Jaccard and vector cosine similarity measure to compute the similarity between the discovered rules. Finally, we group the rules by applying hierarchical clustering. To demonstrate the usability and efficiency of the concept of our technique, we applied it to three publicly available, multiclass cancer gene expression datasets and performed a biomedical literature search to support the effectiveness of our results.

Keywords: Microarray gene expression; association rules; clustering.; similarity measure

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