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
Gene selection for microarray data classification via multi-objective graph theoretic-based method
|Author:||Rostami, Mehrdad1; Forouzandeh, Saman2; Berahmand, Kamal3;|
1Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
2Department of Computer Engineering, University of Applied Science and Technology, Center of Tehran Municipality ICT org., Tehran, Iran
3School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
4Department of Nutrition, Kashan University of Medical Sciences, Kashan, Iran
5Department of engineering physics, Tsinghua University, Beijing, China
6Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022012710459
|Publish Date:|| 2022-01-27
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.
Artificial intelligence in medicine
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
1184 Genetics, developmental biology, physiology
3141 Health care science
This work is supported by the Academy of Finland Profi5 (Project number 326291) on DigiHealth, which gratefully acknowledged.
© 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/).