优化的卷积神经网络使用非洲的优化算法来检测外子.
K Jayasree1, Malaya Kumar Hota2
1Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
Scientific reports
|January 30, 2025
概括
这项研究引入了一种优化卷积神经网络 (optCNN) 用于基因组序列分析,提高了对外子和内子分类的准确性. 非洲优化算法 (AVOA) 增强了该模型,实现了高的成功率.
科学领域:
- 基因组序列分析分析
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 异子检测在基因组分析中至关重要,现有的信号处理方法在准确性方面存在局限性.
- 需要改进的计算模型来增强对外子和内子的识别.
研究的目的:
- 引入一个优化的卷积神经网络 (optCNN) 进行精确的外子和内子分类.
- 通过优化算法来识别最佳的CNN架构和超参数,以改进外子识别.
主要方法:
- 一个优化的卷积神经网络 (optCNN) 已被开发用于分类外子和内子.
- 非洲优化算法 (AVOA) 用于优化CNN的层次架构和超参数.
- 使用 GENSCAN 和 HMR195 数据集来评估性能.
主要成果:
- 优化为AVOA的CNN在GENSCAN训练集上实现了97.95%的成功率,在HMR195数据集上达到95.39%.
- 与使用AUC,F1分数,回忆和精度的最先进方法进行比较,证明了该模型的可靠性.
- 拟议的方法可以自动创建CNN模型,用于表子和内子的分类.
结论:
- 拟议的optCNN模型,由AVOA优化,为表子和内子分类提供了可靠和创新的方法.
- 这种方法显著提高了基因组序列分析的准确性.
- 自动CNN模型生成能力代表了该领域的新进展.
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