Gene selection for microarray data classification via multi-objective graph theoretic-based method
Rostami, Mehrdad; Forouzandeh, Saman; Berahmand, Kamal; Soltani, Mina; Shahsavari, Meisam; Oussalah, Mourad (2021-12-03)
Mehrdad Rostami, Saman Forouzandeh, Kamal Berahmand, Mina Soltani, Meisam Shahsavari, Mourad Oussalah, Gene selection for microarray data classification via multi-objective graph theoretic-based method, Artificial Intelligence in Medicine, Volume 123, 2022, 102228, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2021.102228
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2022012710459
Tiivistelmä
Abstract
In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity.
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