深度学习预测炎症诱导蛋白质编码mRNA在P. gingivalis释放的外膜囊泡
Pradeep Kumar Yadalam1, Raghavendra Vamsi Anegundi1, Muthupandian Saravanan2
1Department of Periodontics, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.
Biomedical engineering and computational biology
|September 2, 2024
概括
这项研究使用深度学习来预测Porphyromonas gingivalis外膜囊泡中的蛋白质编码mRNA. 渐变增强显示了最平衡的性能,表明其对准确的mRNA序列预测的潜力.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- Porphyromonas gingivalis 外膜囊泡 (OMV) 在牙周炎的发病过程中至关重要.
- 在OMV中识别蛋白质编码mRNA序列对于理解它们的功能至关重要.
- 目前用于OMV中的mRNA预测的方法需要改进.
研究的目的:
- 开发和评估机器学习模型,用于预测P. gingivalis OMV中的蛋白质编码mRNA序列.
- 为了比较这个任务的天真贝叶斯,神经网络和渐变增强算法的性能.
- 为准确和可靠的mRNA序列预测确定最有效的模型.
主要方法:
- 来自P. gingivalis OMV的转录组数据被收集并注释.
- 机器学习工具,包括Orange,用于数据预处理和分析.
- 纯粹的贝叶斯模型,神经网络和渐变增强模型被训练并使用交叉验证和ROC曲线进行验证.
- 用AUC,分类准确度和F1分数等指标评估模型性能.
主要成果:
- 梯度提升表现出平衡的性能,AUC为0.72,分类准确度为0.41,F1得分为0.32.
- 神经网络的AUC达到0.721,分类准确度为0.391,F1得分为0.314.
- 纯真贝叶斯的表现较低,AUC为0.701,分类准确度为0.172,F1评分为0.114.1,AUC为0.701,F1评分为0.114.
- 虽然没有发现显著的统计差异,但梯度提升提供了更平衡的精度-回忆关系.
结论:
- 使用机器学习的in silico分析有效预测了P. gingivalis OMVs.中的蛋白质编码mRNA序列.
- 渐变增强成为一个有前途的工具,在实现平衡的预测性能方面超过了天真贝叶斯和神经网络.
- 这些发现表明,渐变增强的潜力是确定细菌OMV中的蛋白质编码mRNA序列的可靠方法.
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