对基因调节网络的多目标共识优化推断:基于偏好的方法
Adrián Segura-Ortiz1, Antonio J Nebro1, José García-Nieto2
1Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain.
Computational biology and chemistry
|December 16, 2025
概括
一个新的偏好引导进化算法,PBEvoGen,通过整合生物知识来增强基因调节网络推断. 它比现有方法提高了准确性和效率,特别是在大型生物网络中.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 基因调节网络 (GRNs) 对于理解生物过程和疾病至关重要.
- 从表达式数据中推断GRNs是计算生物学中的一个基本挑战.
- 现有的GRN推断方法经常受到域偏差的影响,缺乏生物知识整合,限制了它们在生物体中的适用性.
研究的目的:
- 开发一种新的GRN推理方法,解决现有方法的局限性.
- 将生物知识和偏好导向的选择机制整合到一个多目标的进化算法中.
- 提高推断的GRNs的准确性,效率和生物相关性.
主要方法:
- 提出了偏好指南选择机制,以将搜索指向生物相关地区.
- 将这个机制集成到MO-GENECI中,这是一个多目标进化算法 (PBEvoGen).
- 从DREAM3,DREAM4基准和TFLink数据库使用AUROC和AUPR指标对43个GRNs进行了PBEvoGen的评估.
主要成果:
- 在网络质量和准确性方面,PBEvoGen超过了原来的MO-GENECI算法.
- 在43个基准网络中实现了0.67的平均AUROC和0.23的AUPR.
- 演示了性能改进,减少了计算力度,提高了准确性,特别是在大型网络中.
结论:
- 专家知识和进化算法的结合为GRN推理提供了强大的和高效的方法.
- 在推断基因调节网络方面,PBEvoGen提供了显著的进步,具有更高的准确性和生物相关性.
- 开发的软件通过GitHub和PyPI公开提供,以实现更广泛的可访问性和应用.
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