使用修改的微分进化优化算法的优化深森林算法:宿主-病原体蛋白质-蛋白质相互作用预测的一个案例
Jerry Emmanuel1,2,3, Itunuoluwa Isewon1,2,3, Jelili Oyelade1,2,3
1Department of Computer and Information Sciences, Covenant University, Ota, Nigeria.
Computational and structural biotechnology journal
|February 25, 2025
概括
一种新的修改的差异进化 (DE) 方法增强了深森林模型的宿主-病原体蛋白质-蛋白质相互作用预测,提高了准确性和效率.
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
- 机器学习 机器学习
背景情况:
- 深森林模型提供自适应特征学习,但受到手动超参数调整和低效的困扰.
- 贝叶斯优化是一种标准的超参数调整方法,通常由演化算法如差异演化 (DE) 增强.
- 传统的DE方法随机选择矢量,从而在优化中获得次优化解决方案.
研究的目的:
- 开发一个修改的差异进化 (DE) 获取函数,以在深森林模型中改进超参数优化.
- 通过使用优化的深森林模型,提高对宿主-病原体蛋白质-蛋白质相互作用的预测.
- 为了更有效的优化,解决DE中随机向量选择的局限性.
主要方法:
- 开发了一种经过修改的DE获取函数,使用加权和自适应的捐赠载体技术.
- 这种优化的DE方法被集成到Deep Forest模型中,用于自动超参数调.
- 该模型在人类Plasmodium falciparum蛋白序列数据上使用10倍交叉验证进行了评估.
主要成果:
- 优化的深森林模型实现了89.3%的准确度,85.4%的灵敏度和91.6%的精度.
- 该模型在所有评估指标中都超过了标准优化方法和其他机器学习模型.
- 预测了七种新的宿主-病原体相互作用,该模型被部署为web应用程序.
结论:
- 修改后的DE获取功能显著提高了深森林模型对宿主病原体PPI预测的性能.
- 开发的方法为复杂的生物预测中超参数优化提供了一种高效和准确的方法.
- 可访问的Web应用程序促进了优化模型的进一步研究和应用.
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