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. 2022 Mar 15;17(3):e0265351.
doi: 10.1371/journal.pone.0265351. eCollection 2022.

Gene selection using pyramid gravitational search algorithm

Amirhossein Tahmouresi  1 , Esmat Rashedi  2 , Mohammad Mehdi Yaghoobi  3 , Masoud Rezaei  1
Affiliations

Gene selection using pyramid gravitational search algorithm

Amirhossein Tahmouresi et al. PLoS One. .

Abstract

Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful in terms of patient's survival and response to therapy, protein-protein interaction network analysis has been applied on the genes. An interesting pattern was emerged when examining the genetic network. HSP90AA1, PTK2 and SRC genes were amongst the top-rated bottleneck genes, and DNA damage, cell adhesion and migration pathways are highly enriched in the network.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Gene selection using PGSA.
Fig 2
Fig 2. Pseudo code of gene selection using pyramid IBGSA.
Fig 3
Fig 3. Confusion matrix of optimization algorithms.
Fig 4
Fig 4. Accuracy (%) of PGSA during thirty independent runs.
The Y and X axes imply the accuracy and iteration respectively. The bubble size is correlated with the number of genes; the bigger the bubble, the higher the number of genes. The best model was reached at the 18th run with 84.5% accuracy and 73 genes.
Fig 5
Fig 5. Bottleneck subnetwork constructed based on PGSA selected genes in breast cancer.
Red and yellow colors indicate higher and lower bottleneck scores in the network, respectively.
See this image and copyright information in PMC

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