组合循环神经网络与基于鱼优化算法的DNA序列分类,用于医学应用
1Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Soft computing
|June 26, 2023
概括
这项研究引入了一种使用鱼优化的新方法,用于基因表达数据中的特征选择. 该方法通过整体循环神经网络提高了病原体检测的准确性,达到99.59%的精度.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 生物医学和临床数据收集在数据驱动的时代激增.
- 脱氧核糖核酸 (DNA) 基因表达数据集对于通过生物标志物识别病原体至关重要.
- 与元启发相关的特征选择 (FS) 对于管理大型基因数据集至关重要.
研究的目的:
- 在高维度 (HD) 微阵列数据中应用鱼优化算法 (WOA) 进行特征选择.
- 开发一个集体循环神经网络 (ERNN) 用于分类选定的基因表达数据.
- 评估ERNN的性能与现有的先进的病原体检测方法相比.
主要方法:
- 利用鱼优化算法 (WOA) 来从高清微阵列数据集中有效地选择特征.
- 开发了一个集体循环神经网络 (ERNN),集成长期短期记忆 (LSTM),双向LSTM和封闭循环单元 (GRU).
- 使用拟议的ERNN模型对所选基因特征进行分类.
主要成果:
- WOA有效地从大型特征集中过了相关的基因,减少了计算负载.
- ERNN模型在对基因表达数据的分类方面取得了高性能.
- 提出的ERNN方法获得了99.59%的精度和99.59%的准确性.
结论:
- 鱼优化算法在基因表达数据分析中的特征选择中是有效的.
- 整体循环神经网络在使用基因组生物标志物检测病原体方面表现出卓越的性能.
- 这种综合方法为准确和高效地分析生物医学数据提供了一个有希望的策略.
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