Mutational Slime Mould Algorithm for Gene Selection

Abstract

A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

“…Another feature selection method is based on SMA which is a meta-heuristic algorithm based on the oscillation mode of slime mold in nature. This algorithm is designed for engineering problems and continues global optimization and has shown reasonable performance for feature selection in previous research [ 42 , 43 ]. The third feature selection method employed is ABC, which is based on ants' behavior in finding food.…”

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…Deng L et al [55] presented an MSMA in which a mutation operator was added to generate a new search equation to maintain the balance of exploitation and exploration, and an adaptive mutation probability was constructed to avoid premature convergence and maintain the population diversity. Qiu F et al [56] proposed an improved algorithm (ISMA) by combining two strategies of Cauchy and crossover mutations based on the DE, to promote the coordination of global exploration and local exploitation.…”

“…Ghiasi Ramin et al [54] introduced a binary SMA (ABSMA) to better classify the structural damage that occurred. Feng Qiu et al [56] converted an SMA into a binary version with a transfer function and used it for gene selection purposes. Feng Qiu et al [69] also mapped the proposed GLSMA to a binary space via the transformation function and applied for the feature selection.…”

“…Ramin Ghiasi et al [54] employed a binary SMA (BSMA) for feature subset selection to improve the performance of structural damage classifications. Qiu F et al [56] proposed a wrapper gene selection method based on BISMA and assessed it in 14 gene expression datasets. Yang P et al [62] proposed a load identification method based on the improved slime mould algorithmgeneralized regression neural network (ISMA-GRNN) and the ISMA-GRNN achieved higher accuracy and precision values for load identifications obtained from the simulation results.…”