Rostami, Mehrdad, Oussalah, Mourad (2021) Gene selection for cancer diagnosis via iterative graph clustering-based approach. In: Peter Lucas & Fabio Stella (eds.) CEUR workshop proceedings Vol. 3060, 2021 Workshop on Towards Smarter Health Care: Can Artificial Intelligence Help?, SMARTERCARE 2021, (pp. 1-6). http://ceur-ws.org/Vol-3060/paper-1.pdf
Gene selection for cancer diagnosis via iterative graph clustering-based approach
|Author:||Rostami, Mehrdad1; Oussalah, Mourad1,2|
1Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
2Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022032324659
RWTH Aachen University,
|Publish Date:|| 2022-03-23
The development of microarray devices has led to the accumulation of DNA microarray datasets. Through this technological advance, physicians are able to examine various aspects of gene expression for cancer diagnosis. As data accumulation rapidly increases, the task of machine learning faces considerable challenges for high-dimensional DNA microarray data classification. Gene selection is a popular and powerful approach to deal with these high-dimensional cancer data. In this paper, a novel graph clustering-based gene selection approach is developed. The developed approach has two main objectives, consisting of relevance maximization and redundancy minimization of the selected genes. In this method, in each iteration, one subgraph is extracted, and then among the existing genes in this cluster, appropriate genes are selected using filter-based measure. The reported results on five cancer datasets indicate that the developed gene selection approach can improve the accuracy of cancer diagnosis.
CEUR workshop proceedings
|Pages:||1 - 6|
2021 Workshop on Towards Smarter Health Care: Can Artificial Intelligence Help?, SMARTERCARE 2021
|Host publication editor:||
Workshop on Towards Smarter Health Care
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
217 Medical engineering
This project is supported by the Academy of Finland Profi5 DigiHealth project, which is gratefully acknowledged.
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).