As a well-established multidrug combinations schema, traditional Chinese medicine (herbal prescription) has been used for thousands of years in real-world clinical settings. This paper uses a complex network approach to investigate the regularities underlying multidrug combinations in herbal prescriptions. Using five collected large-scale real-world clinical herbal prescription datasets, we construct five weighted herbal combination networks with herb as nodes and herbal combinational use in herbal prescription as links. We found that the weight distribution of herbal combinations displays a clear power law, which means that most herb pairs were used in low frequency and some herb pairs were used in very high frequency. Furthermore, we found that it displays a clear linear negative correlation between the clustering coefficients and the degree of nodes in the herbal combination network (HCNet). This indicates that hierarchical properties exist in the HCNet. Finally, we investigate the molecular network interaction patterns between herb related target modules (i.e., subnetworks) in herbal prescriptions using a network-based approach and further explore the correlation between the distribution of herb combinations and prescriptions. We found that the more the hierarchical prescription, the better the corresponding effect. The results also reflected a well-recognized principle called “Jun-Chen-Zuo-Shi” in TCM formula theories. This also gives references for multidrug combination development in the field of network pharmacology and provides the guideline for the clinical use of combination therapy for chronic diseases.

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.

Gene selection is applied to reduce the number of genes in many applications where gene expression has a high dimension. Existing gene selection methods focus on finding relevant genes, but they often ignore the redundancy among the genes. A novel framework is presented which integrates the removal of feature irrelevance and detection of feature redundancy. The proposed framework firstly removes the irrelevant genes based on the popular gene selection methods (e.g., information gain). And a sparse representation model is designed for the left genes, which aims at removing the redundant genes. Finally cancer prediction is done based on the selected gene space with the classification algorithms. A series of experiments on real data sets have shown that the proposed framework outperforms the existing typical gene selection methods.