智能模型基于PPI的序列预测,使用AISSO深度概念与超参数调过程.
Preeti Thareja1, Rajender Singh Chhillar1, Sandeep Dalal1
1DCSA, Maharshi Dayanand University, Rohtak, Haryana, India.
Scientific reports
|September 18, 2024
概括
一种新的Aquila影响鱼气味 (AISSO) 模型提高了蛋白质-蛋白质相互作用 (PPI) 预测准确度的88%. 这种依赖序列的方法改进了传统的生物解释方法.
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
- 机器学习在生物学中的应用
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对于理解生物功能至关重要.
- 现有的使用多种数据和机器学习的PPI预测方法需要提高性能.
研究的目的:
- 开发一个依赖于序列的PPI预测模型,以提高准确性.
- 引入一种混合预测技术,采用一种新的优化算法.
主要方法:
- 使用基于序列的特征提取,基因本体学,以及改进的语义相似性特征.
- 使用混合神经网络 (改进的循环神经网络,深度信念网络) 进行预测,并结合得分水平.
- 使用阿奎拉影响鱼气味 (AISSO) 算法优化神经网络重量.
主要成果:
- 开发的基于AISSO的模型在PPI预测中达到约88%的准确性.
- 这种表现明显超过了传统的预测方法.
结论:
- 基于AISSO的混合预测模型为依赖序列的PPI预测提供了精确有效的方法.
- 这种方法对推进生物活动解释有前途.
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