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Novel Two-Stage Analytic Approach in Extraction of Strong Herb-Herb Interactions in TCM Clinical Treatment of Insomnia

机译:中药临床治疗失眠中强药草相互作用的新型两阶段分析方法

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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 factors, 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,我们旨在研究中草药中强烈的草药-草药相互作用,以有效治疗失眠。鉴于草药相互作用的提取由于其研究因素之间的非线性相互作用而与基因上位性研究非常相似,因此我们建议应用多维度降维(MDR)技术,该方法在发现生物医学领域的隐藏相互作用模式方面非常有用。但是,MDR由于在处理过程中详尽地枚举因子组合而导致计算量大。为了解决此缺点,我们引入了一种两阶段分析方法,该方法首先使用分层核心子网络分析来预先选择具有高可能性参与药草相互作用的草药子集,然后再应用MDR进行检测预选子集中的强烈属性交互。实验评估证实,该方法能够在具有较高预测准确性的高维中医失眠数据集中检测有效的高阶草药-草药相互作用模型。

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