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Multiscale backbone based network comparison algorithm for effective herbal interaction analysis

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6 Author(s)
Ninglin Du ; School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China ; Xuezhong Zhou ; Runshun Zhang ; Caiyan Jia
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Network modeling and analysis have been developed as one of the promising approaches for exploring the regularities behind the phenomena of complex organization and interactions in many significant fields. Traditional Chinese medicine (TCM) is a kind of holistic medical science, usually in whose clinical setting herb prescriptions consisting of several distinct herbs were used for individualized patients to get the maximum effectiveness. Detecting the significant herb interactions with effectiveness for some specific disease conditions is an important issue for both TCM clinical treatment and novel drug development. By modeling herb prescriptions as herb interaction network, in this paper, we propose a network comparison method based on multiscale backbone algorithm (msbNC) to discover the herbal interactions from one herb network that differ significantly with respect to a referenced herb network according to a null model. This method could easily be used to find the significant effective herbal interactions while incorporating appropriate outcome variables to construct coupled herb networks (one network is constructed from herb prescriptions with good outcome, while another one is from herb prescriptions with bad outcome). Using two herb prescription data sets from the outpatient cases of highly-experienced TCM physicians for insomnia treatment, we applied msbNC method to detect significant herbal interactions in the herb prescriptions of two TCM physicians and two distinct outcomes. The results showed that msbNC could distinguish clinically meaningful herbal interactions from these data sets. Therefore, the proposed method: msbNC coupled with network modeling could be used as a promising approach for effective herb interactions discovery from large-scale clinical data.

Published in:

2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)  (Volume:4 )

Date of Conference:

15-17 Oct. 2011