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 subse1of 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.
Multifactor Dimensionality Reduction Features Selection Traditional Chinese Medicine Insomnia Network Analysis Herb Interactions
Xuezhong Zhou Josiah Poon Paul Kwan Runshun Zhang Yinhui Wang Simon poon Baoyan Liu Daniel Sze
School of Computer and Information Technology,Beijing Jiaotong University,Beijing,China School of Information Technologies,University of Sydney,Sydney,Australia School of Science and Technology,University of New England,Armidale,Australia Guanganmen Hospital,China Academy of Chinese Medical Sciences,Beijing,China China Academy of Chinese Medical Sciences,Beijing,China Department of Health Technology and Informatics,Hong Kong Polytechnic University,Hong Kong