title: Chemometrics-based approach to feature selection of chromatographic profiles and its application to search active fraction of herbal medicine.
abstract: In our previous report (J Pharmaceut Biomed 56 (2011) 443-447), a support vector machine (SVM)-based pharmacodynamic model was established for predicting active fractions of herbal medicines (HMs), where information contents embedded in the chromatograms of the fractions were represented with the peak areas. However, in this representation the global characteristics of the chromatograms were completely missed, which is definitely contrary to the global and holistic views in theories of HMs and undoubtedly reduce the success rate of this model. To deal with the challenge, two chemometrics methods, that is, minimum redundancy maximum relevance (mRMR) and particle swarm optimizer (PSO), were applied in this article for feature selection of the whole chromatograms, and the PSO was also used to tune the SVM parameters. As a case, a sample HM, that is, Xiangdan injection, was investigated. The predictive accuracy was fully evaluated and compared with those by other popular and reported methods. Furthermore, the confirmation on the independent predicting set exhibited that the predicted bioactivities were well consistent with the experimental values. The important potential application of the present model is to be extended to help search active fractions of other HMs.
authors: School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, China. cep02cc@gmail.com
authors: Chao
authors: C
authors: Chen
authors: Jie
authors: J
authors: Yuan
authors: Xiao-Jie
authors: XJ
authors: Li
authors: Zhi-Bin
authors: ZB
authors: Shen
authors: Dao-Hai
authors: DH
authors: Yu
authors: Jun-Fang
authors: JF
authors: Zhu
authors: Fan-Lin
authors: FL
authors: Zeng
mesh_terms: D002851:Chromatography, High Pressure Liquid; D029001:Herbal Medicine / Q000592:standards*; D008962:Models, Theoretical; D010946:Plants, Medicinal / Q000737:chemistry; D060388:Support Vector Machine