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首个用于识别肝素结合蛋白的计算框架

Wen Zhu^1,2,3, Shi-Shi Yuan⁴, Jian Li⁵

¹Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China.

Diagnostics (Basel, Switzerland)

|July 29, 2023

概括

这项研究引入了第一个机器学习框架,用于识别肝素结合蛋白 (HBP),这是传染病的关键生物标志物. 开发的支持矢量机模型准确识别HBP,有助于传染病研究.

科学领域:

生物化学生化学
计算生物学计算生物学
传染病研究传染病研究

背景情况:

肝素结合蛋白 (HBP) 是来自中性粒细胞的阴抗菌蛋白.
乙型肝炎蛋白作为传染病的关键生物标志物.
准确识别HBP对于研究传染病至关重要.

研究的目的:

开发第一个基于机器学习的框架,用于准确识别肝素结合蛋白 (HBP).
评估机器学习算法在识别HBP方面的性能.

主要方法:

使用四个序列描述符以数字表示HBP和非HBP样本.
使用支持矢量机 (SVM) 和随机森林 (RF) 算法进行分类.
使用训练数据与十倍交叉验证和独立测试数据集验证模型性能.

主要成果:

基于SVM的分类器显示了HBP识别的最高潜力.
在训练数据上达到0.981±0.028的auROC.
在独立测试数据上获得了95.0%的整体准确性.

结论:

开发的SVM模型是HBP识别的第一个计算框架.
这种模型显示出有很大的潜力,可以帮助研究和诊断传染病.
这些发现鼓励进一步研究用于生物标志物发现的机器学习应用.

关键词:

氨基酸组成的氨基酸组成.组合/转换/分布的情况.双组合的二组合.与预期平均值相比,二的偏差.氨酸结合蛋白质-氨酸结合蛋白质支持矢量机器的支持矢量机器.

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