Shyam Marjit, Trinav Bhattacharyya, Bitanu Chatterjee, and Ram Sarkar.
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. \hl{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.
> ***Index Terms*** — Feature Selection, Genetic Algorithm, Simulated Annealing, Optimization Algorithm, Gene Expression, Microarray Dataset
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@article{MARJIT2023106854,
title = {Simulated annealing aided genetic algorithm for gene selection from microarray data},
author = {Shyam Marjit and Trinav Bhattacharyya and Bitanu Chatterjee and Ram Sarkar},
journal = {Computers in Biology and Medicine},
pages = {106854},
year = {2023},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2023.106854},
url = {https://www.sciencedirect.com/science/article/pii/S0010482523003190},
}Shyam Marjit: [email protected]