学习因果生物学网络与并行殖民地优化算法

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Updated: Jul 18, 2025

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Jihao Zhai1, Junzhong Ji1, Jinduo Liu1

  • 1Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Bioengineering (Basel, Switzerland)
|August 26, 2023
PubMed
概括

本研究介绍了一种新的并行群优化 (PACO) 算法,用于从复杂的生物数据中可靠地学习因果生物网络 (CBN). PACO通过利用全球信息和并行处理来提高准确性和效率,克服现有方法的局限性.

关键词:
在CBNs的融合中,CBNs融合了.有因果关系的生物网络.因果大脑网络是因果大脑网络.因果蛋白信号网络是因果蛋白信号网络.平行殖民地优化平行殖民地优化费洛蒙聚变反应的方法

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