Open Access
ARTICLE
A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma
ZEKUN XIN1,#, YUDAN MA2,#, WEIQIANG SONG3, HAO GAO3, LIJUN DONG3, BAO ZHANG1,*, ZHILONG REN3,*
1 Department of Urology, Aerospace Center Hospital, Beijing, 100049, China
2 Beijing Institute of Technology, Beijing, 100081, China
3 Urology Surgery, Hebei Petro China Central Hospital, Langfang, 065000, China
* Corresponding Authors: BAO ZHANG. Email: ; ZHILONG REN. Email:
(This article belongs to this Special Issue: Application of Deep Learning in Cancer)
BIOCELL 2023, 47(3), 555-567. https://doi.org/10.32604/biocell.2023.026254
Received 26 August 2022; Accepted 17 October 2022; Issue published 03 January 2023
Abstract
Background: Recently, researchers have been attracted in identifying the crucial genes related to cancer, which
plays important role in cancer diagnosis and treatment. However, in performing the cancer molecular subtype
classification task from cancer gene expression data, it is challenging to obtain those significant genes due to the high
dimensionality and high noise of data. Moreover, the existing methods always suffer from some issues such as
premature convergence.
Methods: To address those problems, we propose a new ant colony optimization (ACO)
algorithm called DACO to classify the cancer gene expression datasets, identifying the essential genes of different
diseases. In DACO, first, we propose the initial pheromone concentration based on the weight ranking vector to
accelerate the convergence speed; then, a dynamic pheromone volatility factor is designed to prevent the algorithm
from getting stuck in the local optimal solution; finally, the pheromone update rule in the Ant Colony System is
employed to update the pheromone globally and locally. To demonstrate the performance of the proposed algorithm
in classification, different existing approaches are compared with the proposed algorithm on eight high-dimensional
cancer gene expression datasets.
Results: The experiment results show that the proposed algorithm performs better
than other effective methods in terms of classification accuracy and the number of feature sets. It can be used to
address the classification problem effectively. Moreover, a renal cell carcinoma dataset is employed to reveal the
biological significance of the proposed algorithm from a number of biological analyses.
Conclusion: The results
demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.
Keywords
Cite This Article
XIN, Z., MA, Y., SONG, W., GAO, H., DONG, L. et al. (2023). A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma.
BIOCELL, 47(3), 555–567.