Simulated annealing aided genetic algorithm for gene selection from microarray data

• Published in: Computers in biology and medicine Vol. 158; p. 106854
• Main Authors: Marjit, Shyam, Bhattacharyya, Trinav, Chatterjee, Bitanu, Sarkar, Ram
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2023; Elsevier Limited
• Subjects: Algorithms; Biomarkers; Cluster Analysis; Clustering; Correlation coefficients; Datasets; DNA microarrays; Feature selection; Gene expression; Genes; Genetic algorithm; Genetic algorithms; Heuristic methods; Humans; Internal Medicine; Microarray dataset; Neoplasms - genetics; Oligonucleotide Array Sequence Analysis - methods; Optimization algorithm; Other; Simulated annealing; Simulation; Microarray dataset; Feature selection; Optimization algorithm; Gene expression; Genetic algorithm; Simulated annealing
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.106854

In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic Algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic Algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://github.com/shyammarjit/SAGA. •Application of Simulated Annealing aided Genetic Algorithm to solve the FS problem.•Introduced a new multi-objective fitness function to evaluate a feature subset.•Proposal of a new acceptance probability function in SA and enhancements in GA.•Use of initial feature dropping using MICC for microarray and omics datasets.•Clustering-based population initialization to avoid premature convergence of SAGA.