融合多种算法提升贝叶斯因果网络推断的可靠性
BaMANI: Bayesian Multi-Algorithm causal Network Inference
- 采用集成学习策略，降低单一算法对推断结果的影响
- 通过理论框架与新工具BaMANI实现多算法协同推理
- 在乳腺癌研究中验证，适合生物医学因果分析场景
计算能力的提升使得各学科能够利用贝叶斯网络推断模型变量间的因果关系。尽管已有多种算法被提出以提高推断效率和可靠性，但预测出的因果网络不仅反映生成过程，也带有特定计算算法的隐性影响。受“群体智慧”启发，本文提出一种集成学习方法，用于弱化单个算法对贝叶斯因果网络推断的偏倚。首先阐述该框架的理论基础，随后开发了名为BaMANI（Bayesian Multi-Algorithm causal Network Inference）的新软件工具实现完整流程，并在生物学领域，特别是人类乳腺癌研究中展示了应用案例。
原文摘要 · Abstract (English)
Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also bear an opaque imprint of the specific computational algorithm used. Following a ``wisdom of the crowds" strategy, we developed an ensemble learning approach to marginalize the impact of a single algorithm on Bayesian causal network inference. To introduce the approach, we first present the theoretical foundation of this framework. Next, we present a comprehensive implementation of the framework in terms of a new software tool called BaMANI (Bayesian Multi-Algorithm causal Network Inference). Finally, we describe a BaMANI use-case from biology, particularly within human breast cancer studies.
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