Application of Orthogonal Block Variables and Canonical Correlation Analysis in Modeling Pharmacological Activity of Alkaloids from Plant Medicines

by Qian-Nan Hu, Yi-Zeng Liang, Xiao-Ling Peng, Yin Hong and Lian Zhu

Journal of Data Science, v.1, no.4, 405-423

Abstract

A new kind of orthogonal block variables, derived from subspace projection and canonical correlation analysis, is applied to model pharmaological activity of alkaloids from plant drugs. The alkaloids are grouped into three cases by intravenous, intraperitoneal, and subcutaneous injections. Four block variables (family of variables) investigated in this work are valence molecular connectivity index, alpha kappa index, E-State index and element counts of molecules, respectively. The regression model embracing only few new orthogonal block variables against pharmaological activity shows significant improvement than those, say multiple linear regression (MLR) simply using original variables, principal component regression (PCR) and the ones selecting only one or two of the original family variables, both in fitting and prediction ability of the correlation model. The reason for this might be that the new orthogonal block variables in fact include almost all of the information of the original variables but without collinearity between them.

Homepage | Table of Contents | Full Text of This Article