GRNMOPT:基因调节网络的推断基于多目标优化方法
Heng Dong1, Baoshan Ma1, Yangyang Meng1
1School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
Computational biology and chemistry
|September 28, 2024
概括
GRNMOPT使用普通微分方程 (ODEs) 推断基因调节网络 (GRNs),以优化衰变速率和时间延迟. 这种方法通过有效估计关键监管参数来提高GRN推断的准确性.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因调节网络 (GRN) 的重建对于理解生物过程至关重要.
- 非线性普通微分方程 (ODEs) 对于GRN预测是有效的.
- 在GRNs的ODEs模型中,对衰变速率和时间延迟的系统确定尚未得到充分研究.
研究的目的:
- 为准确的GRN推理开发一个全面的优化框架.
- 在ODE模型中系统估计关键参数,如衰变速率和时间延迟.
- 为了提高GRN重建的准确性.
主要方法:
- 引入了GRNMOPT,这是一种从时间序列和稳定状态数据中推断GRN的新方法.
- 采用了ODE模型,其中包含了真实基因调节表示的衰变速率和时间延迟.
- 利用多目标优化方法同时优化衰变速率和时间延迟,推导出帕雷托最佳集.
- 应用XGBoost用于特征重要性计算,以确定潜在的调节性基因链接.
主要成果:
- 在模拟 (DREAM4) 和真实基因表达数据集 (酵母,IRMA,大肠杆菌) 上,GRNMOPT在各种网络尺度上表现出了值得称赞的性能.
- 交叉验证实验证实了GRNMOPT方法的稳定性.
- 这种方法有效地改善了AUROC和AUPR等准确度指标.
结论:
- 提出了GRNMOPT,这是一种使用多目标优化框架进行GRN推理的新方法.
- GRNMOPT显著提高了GRN推断的准确性.
- 这种方法为推进GRN研究提供了一个强大的工具.
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