Simulated annealing aided genetic algorithm for gene selection from microarray data. (May 2023)

• Record Type: Journal Article
• Authors: Marjit, Shyam; Bhattacharyya, Trinav; Chatterjee, Bitanu; Sarkar, Ram
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: Feature selection -- Genetic algorithm -- Simulated annealing -- Optimization algorithm -- Gene expression -- Microarray dataset; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106854
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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 . Highlights: 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.