ZEKUN XIN1,#, YUDAN MA2,#, WEIQIANG SONG3, HAO GAO3, LIJUN DONG3, BAO ZHANG1,*, ZHILONG REN3,*
BIOCELL, Vol.47, No.3, pp. 555-567, 2023, DOI:10.32604/biocell.2023.026254
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,… More >
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