.png){kind=icon}
.png){kind=icon}
返回
Exploring gene causal interactions using an enhanced constraint-based method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2006.05.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
DNA microarray provides a powerful basis for analysis of gene expression. Bayesian networks, which are based on directed acyclic graphs (DAGs) and can provide models of causal influence, have been investigated for gene regulatory networks. The difficulty with this technique is that learning the Bayesian network structure is an NP-hard problem, as the number of DAGs is superexponential in the number of genes, and an exhaustive search is intractable. In this paper, we propose an enhanced constraint-based approach for causal structure learning. We integrate with graphical Gaussian modeling and use its independence graph as an input of our constraint-based causal learning method. We also present graphical decomposition techniques to further improve the performance. Our enhanced method makes it feasible to explore causal interactions among genes interactively. We have tested our methodology using two microarray data sets. The results show that the technique is both effective and efficient in exploring causal structures from microarray data. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
causal modeling
microarray
interaction analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。