Ensemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints

BMC Bioinformatics
Hao ChenDaniel Sze

Abstract

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. 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 the Cluster of Differentiation 80 (CD80) expression bioactivity of a commonly used herb, Astragali Radix (AR), fr...Continue Reading

References

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Citations

Jan 30, 2016·Phytotherapy Research : PTR·Cláudio Rodrigues Rezende CostaEliete Neves Silva Guerra
Oct 16, 2018·Frontiers in Pharmacology·Yu-Jun TanJie Li

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Methods Mentioned

BETA
PCR

Software Mentioned

Meta
SMLR
PLSR
Learner

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