Abstract
Microarray datasets contain genetic information of patients analysis of which can reveal new findings about the cause and subsequent treatment of any disease. With an objective to extract biologically relevant information from the datasets, many techniques are used in gene analysis. In the paper, the concepts like functional dependency and closure of an attribute of database technology are applied to find the most important gene subset and based on which the samples of the gene datasets are classified as normal and disease samples. The gene dependency is defined as the number of genes dependent on a particular gene using gene similarity measurement on collected samples. The closure of a gene is computed using gene dependency set which helps to know how many genes are logically implied by it. Finally, the minimum number of genes whose closure logically implies all the genes in the dataset is selected for sample classification.
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Das, S., Das, A.K. (2016). Sample Classification Based on Gene Subset Selection. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_24
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DOI: https://doi.org/10.1007/978-81-322-2734-2_24
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