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@article{191026,
        author = {Vinita Singh},
        title = {Motif Discovery in Biological Data Using Simulated Annealing with Neighborhood Search and Iterative Restart Strategy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5532-5534},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191026},
        abstract = {Motif discovery in biological data, such as DNA, RNA, and protein sequences, plays a crucial role in understanding biological functions and regulatory mechanisms. The identification of recurring, biologically significant patterns or motifs is a challenging problem due to the vast search space and the inherent noise in biological datasets. This paper proposes a novel approach to motif discovery that combines the power of simulated annealing (SA) with a neighborhood search and iterative restart strategy to efficiently explore the solution space and avoid local optima.
Simulated annealing is a probabilistic optimization technique inspired by the physical process of annealing, where the system gradually stabilizes to a low-energy state. We enhance this standard method by integrating a neighborhood search mechanism that dynamically explores neighboring motifs to refine the solution space. Additionally, an iterative restart strategy is introduced to overcome premature convergence and increase the likelihood of discovering global optima. The method is designed to handle the combinatorial nature of motif discovery and mitigate issues related to overfitting and underfitting by balancing exploration and exploitation during the search process.},
        keywords = {Motif, Nucleotide Sequences, Simulated Annealing algorithm},
        month = {January},
        }