Evaluation of herbal medicine´s bioactivity capacity prediction algorithms

Author

Hao Chen ; Poon, Josiah ; Poon, Simon ; Lizhi Cui ; Sze, Daniel

Author_Institution

Univ. of Sydney, Sydney, NSW, Australia

fYear

2014

fDate

2-5 Nov. 2014

Firstpage

14

Lastpage

15

Abstract

Currently, there are many multivariate linear regression algorithms being used for predicting the bioactive capacity of herbal formulae or herbal extracts from their chromatographic fingerprints, such as Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Orthogonal Projections to Latent Structures (OPLS) and Elastic Net (EN). In this study, the predicting performance and the complexity of predictive models developed by PCR, PLSR, OPLS, and EN are evaluated and compared using a set of chromatographic fingerprints of Astragali Radix (AR) and their corresponding bioactivity: Cluster of Differentiation 80 (CD80).

Keywords

bioinformatics; chromatography; least squares approximations; patient treatment; principal component analysis; regression analysis; OPLS; PCR; PLSR; astragali radix; chromatographic fingerprints; cluster-of-differentiation 80; elastic net; herbal extracts; herbal formulae; herbal medicine bioactivity capacity prediction algorithms; multivariate linear regression algorithms; orthogonal projections-to-latent structures; partial least squares regression; principal component regression; Australia; Biological system modeling; Complexity theory; Fingerprint recognition; Prediction algorithms; Predictive models; Training; bioactivity prediction; chromatographic fingerprint; evaluation;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on

Conference_Location

Belfast

Type

conf

DOI

10.1109/BIBM.2014.6999313

Filename

6999313