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首页> 外文会议>International Conference on Medical Biometrics >Novel Two-Stage Analytic Approach in Extraction of Strong Herb-Herb Interactions in TCM Clinical Treatment of Insomnia
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Novel Two-Stage Analytic Approach in Extraction of Strong Herb-Herb Interactions in TCM Clinical Treatment of Insomnia

机译：中医临床治疗初始草药互动提取新的两阶段分析方法

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In this paper, we aim to investigate strong herb-herb interactions in TCM for effective treatment of insomnia. Given that extraction of herb interactions is quite similar to gene epistasis study due to non-linear interactions among their study factor s, we propose to apply Multifactor Dimensionality Reduction (MDR) that has shown useful in discovering hidden interaction patterns in biomedical domains. However, MDR suffers from high computational overhead incurred in its exhaustive enumeration of factors combinations in its processing. To address this drawback, we introduce a two-stage analytical approach which first uses hierarchical core sub-network analysis to pre-select the subset of herbs that have high probability in participating in herb-herb interactions, which is followed by applying MDR to detect strong attribute interactions in the pre-selected subset. Experimental evaluation confirms that this approach is able to detect effective high order herb-herb interaction models in high dimensional TCM insomnia dataset that also has high predictive accuracies.
机译：在本文中，我们的目的是探讨有效的治疗失眠的中医中药强，草药相互作用。鉴于中药相互作用的提取是非常相似的基因上位的研究，由于他们的研究因子s之间的非线性相互作用，我们建议采用多因子降维（MDR）已显示在生物医学领域发现隐藏的交互模式很有用。然而，从MDR在在其加工因子的组合其穷举招致高的计算开销缺点。为了解决这个缺点，我们介绍它首先采用分层铁芯子网络分析预先选择参加香草药草相互作用，随后是应用MDR检测具有高概率药材的子集的两阶段分析方法在预选择的子集强属性的相互作用。实验评价证实，这种方法能够检测到高维中医失眠数据集也具有很高的预测精度有效高阶香草药草相互作用模型。

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