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Cancer gene selection with adaptive optimization spiking neural P systems and hybrid classifiers

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Abstract

The selection of disease-causing genes from gene expression and methylation data is a great benefit for cancer diagnosis and treatment, but it also faces the limitations due to single classifier addressing different datasets and exponential growth in search space. To address the problem, a hybrid filter-wrapper approach, an adaptive optimization spiking neural P systems and hybrid classifier (AOSNPS-HC), is proposed for cancer gene selection in this paper. More specifically, a hybrid classifier consisting of support vector machines and K-nearest neighbors is introduced using the competitive strategy to balance the advantages and disadvantages of the both traditional classifier for a specific gene dataset. At the same time, an adaptive optimization spiking neural P system (AOSNPS) is used to speed up the search of the best gene solution in the exponentially growing search space. Experimental results on six data sets, Leukemia, Colon cancer, SRBCT, Prostate-cancer, Brain Tumors and Lung-cancer, show that AOSNPS-HC is superior to or competitive to ten methods in literature in terms of gene feature and classification accuracy.

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Data availability

The six microarray datasets can be freely obtained at https://file.biolab.si/biolab/supp/bicancer/projections/info/leukemia.html.

Abbreviations

CA::

Classification accuracy

SVM::

Support vector machine

kNN::

K-nearest neighbors

MC::

Membrane computing

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (61972324), Sichuan Science and Technology Program (2023NSFSC1985, 2023YFG0046, 2022YFG0181) and Research Fund of Chengdu University of Information Technology (KYTZ202149, KYTD202212)

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  1. Yangheng Hu, Jianping Dong, and Yijin Wu have contributed equally to this work.

Authors and Affiliations

  1. School of Automation, Chengdu University of Information Technology, Chengdu, 610225, China

    Yangheng Hu, Gexiang Zhang, Yijin Wu & Ming Zhu

  2. School of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China

    Jianping Dong

  3. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, China

    Haina Rong

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  1. Yangheng Hu
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Hu, Y., Dong, J., Zhang, G. et al. Cancer gene selection with adaptive optimization spiking neural P systems and hybrid classifiers. J Membr Comput 5, 238–251 (2023). https://doi.org/10.1007/s41965-023-00133-w

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