University of Oulu

Saeid Azadifar, Mehrdad Rostami, Kamal Berahmand, Parham Moradi, Mourad Oussalah, Graph-based relevancy-redundancy gene selection method for cancer diagnosis, Computers in Biology and Medicine, Volume 147, 2022, 105766, ISSN 0010-4825,

Graph-based relevancy-redundancy gene selection method for cancer diagnosis

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Author: Azadifar, Saeid1; Rostami, Mehrdad2; Berahmand, Kamal3;
Organizations: 1Department of Computer Engineering, University of Khajeh Nasir Toosi, Tehran, Iran
2Centre for Machine Vision and Signal Processing, University of Oulu, Oulu, Finland
3School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Australia
4Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
5Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.3 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2022-11-23


Nowadays, microarray data processing is one of the most important applications in molecular biology for cancer diagnosis. A major task in microarray data processing is gene selection, which aims to find a subset of genes with the least inner similarity and most relevant to the target class. Removing unnecessary, redundant, or noisy data reduces the data dimensionality. This research advocates a graph theoretic-based gene selection method for cancer diagnosis. Both unsupervised and supervised modes use well-known and successful social network approaches such as the maximum weighted clique criterion and edge centrality to rank genes. The suggested technique has two goals: (i) to maximize the relevancy of the chosen genes with the target class and (ii) to reduce their inner redundancy. A maximum weighted clique is chosen in a repetitive way in each iteration of this procedure. The appropriate genes are then chosen from among the existing features in this maximum clique using edge centrality and gene relevance. In the experiment, several datasets consisting of Colon, Leukemia, SRBCT, Prostate Tumor, and Lung Cancer, with different properties, are used to demonstrate the efficacy of the developed model. Our performance is compared to that of renowned filter-based gene selection approaches for cancer diagnosis whose results demonstrate a clear superiority.

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Series: Computers in biology and medicine
ISSN: 0010-4825
ISSN-E: 1879-0534
ISSN-L: 0010-4825
Volume: 147
Article number: 105766
DOI: 10.1016/j.compbiomed.2022.105766
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: This research is connected to the DigiHealth-project, a strategic profiling project at the University of Oulu. The project is supported by the Academy of Finland (project number 326291) and the University of Oulu Academy of Finland Profi5 on DigiHealth.
Copyright information: © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (