Multi-Source Causal Analysis: Learning Bayesian Networks from Multiple Datasets
Multi-Source Causal Analysis: Learning Bayesian Networks from Multiple Datasets
复制标题
多源因果分析：从多个数据集中学习贝叶斯网络
DOI：
10.1007/978-1-4419-0221-4_56
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
A. Mariglis
中科院分区：
文献类型：
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作者：
I. Tsamardinos;A. Mariglis
我们认为，因果关系即使不是一个必要的概念，也是一个有用的概念，以允许对多个数据源进行综合分析。具体地说，我们证明它能够从(A)在不同实验条件下获得的数据，(B)在不同变量集上的数据，以及(C)在语义上相似的变量上的数据中学习因果关系，但由于各种技术原因，这些变量不能被拉在一起。尤其是后一种情况，经常发生在分析多个基因表达数据集的设置中。对于上述情况(A)和(B)，已经有解决这些问题的初步算法，尽管有一些限制，而对于情况(C)，我们开发和评估了一种新的方法。初步的经验结果证明，与从每个单独的数据集学习相比，使用我们的方法组合多个来源时，因果关系的学习性能有所提高。在以上讨论的背景下，我们引入了多来源因果分析(MSCA)问题，定义为从多个数据和知识来源推断和归纳因果知识的问题。MSCA的宏伟愿景是实现可用数据的自动化或半自动大规模集成，以构建涉及人类概念的重要部分的因果模型。
We argue that causality is a useful, if not a necessary concept to allow the integrative analysis of multiple data sources. Specifically, we show that it enables learning causal relations from (a) data obtained over different experimental conditions, (b) data over different variable sets, and (c) data over semantically similar variables that nevertheless cannot be pulled together for various technical reasons. The latter case particularly, often occurs in the setting of analyzing multiple gene-expression datasets. For cases (a) and (b) above there already exist preliminary algorithms that address them, albeit with some limitations, while for case (c) we develop and evaluate a new method. Preliminary empirical results provide evidence of increased learning performance of causal relations when multiple sources are combined using our method versus learning from each individual dataset. In the context of the above discussion we introduce the problem of Multi-Source Causal Analysis (MSCA), defined as the problem of inferring and inducing causal knowledge from multiple sources of data and knowledge. The grand vision of MSCA is to enable the automated or semi-automated, large-scale integration of available data to construct causal models involving a significant part of human concepts.