OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.

Journal: Interdisciplinary sciences, computational life sciences
Published Date: Jun 10, 2025

Abstract

Identifying DNA binding sites remains a critical task in bioinformatics, with applications ranging from gene regulation studies to drug design. Although progress has been made in computational techniques, we still face challenges such as data complexity and prediction accuracy. In this paper, we introduce OptimDase, a new algorithm. It integrates feature encoding with optimum decision-making frameworks to improve DNA binding site prediction. OptimDase integrates multi-scale scanning and feature selection strategies, making it highly effective for both classification and regression tasks. Our experiments demonstrate that OptimDase achieves superior performance with an accuracy of 0.8943 in classification tasks and an RMSE of 0.0054 in regression tasks, outperforming existing algorithms in key evaluation metrics. These results highlight OptimDase's portability and robustness, making it an effective solution for identifying DNA binding sites and advancing the applications of drug design.

Authors

  • Zhendong Liu
    Nanjing University, 163 Xianlin Avenue, Qixia District, Nanjing, Jiangsu 210023, China. Electronic address: dz20330019@smail.nju.edu.cn.
  • Jun S Liu
    Department of Statistics, Harvard University, Cambridge, 02138, USA. jliu@stat.harvard.edu.
  • Dongqing Wei
    School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. dqwei@sjtu.edu.cn.
  • Rongjun Man
    Department of Otolaryngology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China. manrongjun@sdfmu.edu.cn.
  • Jiamin Jiang
    School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China.
  • Bofeng Zhang
    School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China.
  • Liping Li
    School of Public Health, Key Laboratory of Environment and Human Health of Hebei Medical University Shijiazhuang 050017 China xuxd@hebmu.edu.cn.
  • Zhiyong Zhao
    Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hang Zhou, China.

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