Ensemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints. Issue 12 (December 2015)

• Record Type: Journal Article
• Authors: Chen, Hao; Poon, Josiah; Poon, Simon; Cui, Lizhi; Fan, Kei; Sze, Daniel
• Is Part Of: BMC bioinformatics. Volume 16:Issue 12(2015)
• Page Start: 1
• Page End: 8
• Publication Date: 2015-12
• Subjects: Chromatographic Fingerprints -- Bioactivity Prediction -- Ensemble Learning; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S12-S4
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Recent quality control of complex mixtures, including herbal medicines, is not limited to chemical chromatographic definition of one or two selected compounds; multivariate linear regression methods with dimension reduction or regularisation have been used to predict the bioactivity capacity from the chromatographic fingerprints of the herbal extracts. The challenge of this type of analysis requires a multi-dimensional approach at two levels: firstly each herb comprises complex mixtures of active and non-active chemical components; and secondly there are many factors relating to the growth, production, and processing of the herbal products. All these factors result in the significantly diverse concentrations of bioactive compounds in the herbal products. Therefore, it is imminent to have a predictive model with better generalisation that can accurately predict the bioactivity capacity of samples when only the chemical fingerprints data are available. Results In this study, the algorithm of Stacking Multivariate Linear Regression (SMLR) and a few other commonly used chemometric approaches were evaluated. They were to predict theCluster of Differentiation 80 (CD80) expression bioactivity of a commonly used herb, Astragali Radix (AR), from the corresponding chemical chromatographic fingerprints. SMLR provides a superior prediction accuracy in comparison with the other multivariate linear regression methods of PCR, PLSR, OPLS and EN in terms ofMSE test and the goodness of prediction of test samples. Conclusions SMLR is a better platform than some multivariate linear regression methods. The first advantage of SMLR is that it has better generalisation to predict the bioactivity capacity of herbal medicines from their chromatographic fingerprints. Future studies should aim to further improve the SMLR algorithm. The second advantage of SMLR is that single chemical compounds can be effectively identified as highly bioactive components which demands further CD80 bioactivity confirmation..