Probability Based Most Informative Gene Selection From Microarray Data

Probability Based Most Informative Gene Selection From Microarray Data

Sunanda Das (Neotia Institute of Technology, Management and Science, Department of Computer Science and Engineering, Diamond Harbour, W. B., India) and Asit Kumar Das (Indian Institute of Engineering Science and Technology, Shibpur, Department of Computer Science and Technology, Howrah,W. B., India)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/IJRSDA.2018010101
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Microarray datasets have a wide application in bioinformatics research. Analysis to measure the expression level of thousands of genes of this kind of high-throughput data can help for finding the cause and subsequent treatment of any disease. There are many techniques in gene analysis to extract biologically relevant information from inconsistent and ambiguous data. In this paper, the concepts of functional dependency and closure of an attribute of database technology are used for finding the most important set of genes for cancer detection. Firstly, the method computes similarity factor between each pair of genes. Based on the similarity factors a set of gene dependency is formed from which closure set is obtained. Subsequently, conditional probability based interestingness measurements are used to determine the most informative gene for disease classification. The proposed method is applied on some publicly available cancerous gene expression dataset. The result shows the effectiveness and robustness of the algorithm.
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The Proposed Algorithm

The data studied here for the proposed method is represented in the form of decision table. One decision table can be represented as S = (U, C, D), where U is the set of samples, C= {g1, g2, …, gn} is the condition attribute set and D = {d1, d2} is the decision attribute set. Every gene expression data can be represented with the decision table which is shown in Table 1.

Table 1.
Microarray dataset decision table
SamplesCondition attributes (genes)Decision attributes (classes)
Gene 1Gene 2Gene nClass label
1g(1, 1)g(1, 2)g(1, n)Class (1)
2g(2, 1)g(2, 2)g(2, n)Class (2)
mg(m, 1)g(m, 2)g(m, n)Class (m)

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