Structural learning of Bayesian networks (BNs) from observational data has gained increasing applied use and attention from various scientific and industrial areas. The mathematical theory of BNs and their optimization is well developed. Although there are several open-source BN learners in the public domain, none of them are able to handle both small and large feature space data and recover network structures with acceptable accuracy. bAIcis^® is a novel BN learning and simulation software from BERG. It was developed with the goal of learning BNs from “Big Data” in health care, often exceeding hundreds of thousands features when research is conducted in genomics or multi-omics. This article provides a comprehensive performance evaluation of bAIcis and its comparison with the open-source BN learners. The study investigated synthetic datasets of discrete, continuous, and mixed data in small and large feature space, respectively. The results demonstrated that bAIcis outperformed the publicly available algorithms in structure recovery precision in almost all of the evaluated settings, achieving the true positive rates of 0.9 and precision of 0.8. In addition, bAIcis supports all data types, including continuous, discrete, and mixed variables. It is effectively parallelized on a distributed system and can work with datasets of thousands of features that are infeasible for any of the publicly available tools with a desired level of recovery accuracy.

中文翻译：

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bAIcis：一种新颖的贝叶斯网络结构学习算法及其针对开源软件的综合性能评估。

从观测数据进行贝叶斯网络（BNs）的结构学习已得到各个科学和工业领域越来越多的应用使用和关注。BN的数学理论及其优化已得到很好的发展。尽管在公共领域有几个开源的BN学习者，但他们中没有一个能够处理大小空间特征数据，也无法以可接受的精度恢复网络结构。bAIcis ^®是BERG一种新型的BN学习和模拟软件。它的开发目的是从医疗保健的“大数据”中学习BN，在进行基因组学或多组学研究时，经常会超过数十万个功能。本文提供了对bAIcis的综合性能评估并将其与开源BN学习者进行比较。该研究分别研究了在小特征空间和大特征空间中离散，连续和混合数据的综合数据集。结果表明，在几乎所有评估的设置中，bAIcis在结构恢复精度方面均优于公开可用的算法，实现了0.9的真实阳性率和0.8的精度。此外，bAIcis支持所有数据类型，包括连续，离散和混合变量。它可以在分布式系统上有效地并行化，并且可以处理具有数千种功能的数据集，而这些功能对于任何公开可用的工具都是不可行的，并具有所需的恢复精度。