A novel multi-target regression framework for time-series prediction of drug efficacy

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

[1]  Y. Haitovsky.  On multivariate ridge regression , 1987 .

[2]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[3]  Hassan Golmohammadi,et al.  Prediction of octanol–water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network , 2009, J. Comput. Chem..

[4]  Tormod Næs,et al.  Artificial Neural Networks in Multivariate Calibration , 1993 .

[5]  Timo Similä,et al.  Input selection and shrinkage in multiresponse linear regression , 2007, Comput. Stat. Data Anal..

[6]  Ivan Bratko,et al.  Improving vehicle aeroacoustics using machine learning , 2012, Eng. Appl. Artif. Intell..

[7]  Lauri Niinistö,et al.  Application of PLS multivariate calibration for the determination of the hydroxyl group content in calcined silica by DRIFTS , 2000 .

[8]  Tapio Elomaa,et al.  Multi-target regression with rule ensembles , 2012, J. Mach. Learn. Res..

[9]  Junbin Gao,et al.  A novel approach in discovering significant interactions from TCM patient prescription data , 2011, Int. J. Data Min. Bioinform..

[10]  Alison J. Burnham,et al.  LATENT VARIABLE MULTIVARIATE REGRESSION MODELING , 1999 .

[11]  T. Sumida,et al.  Efficacy prediction of cevimeline in patients with Sjögren’s syndrome , 2007, Clinical Rheumatology.

[12]  Xuezhong Zhou,et al.  Discovery of regularities in the use of herbs in chinese medicine prescriptions , 2012, Chinese Journal of Integrative Medicine.

[13]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[14]  Luis Alonso,et al.  Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation , 2011, IEEE Geoscience and Remote Sensing Letters.

[15]  J. Friedman.  Stochastic gradient boosting , 2002 .

[16]  Yu Dong,et al.  [Experimental studies of effects of Wujiwan extracts in different compatibilities on motility of isolated colon in guinea pig]. , 2007, Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica.

[17]  Qing Yang,et al.  [Effects of Wuji pill compound with different compatibility on cytochrome P450 CYP3A1/3A2 in rat liver microsomes in vitro]. , 2010, Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica.

[18]  Grigorios Tsoumakas,et al.  Multi-target Regression via Random Linear Target Combinations , 2014, ECML/PKDD.

[19]  Yuan Gao,et al.  Simultaneous Determination of Seven Bioactive Compounds in Wuji Pill by HPLC , 2012 .

[20]  Desire L. Massart,et al.  The optimal brain surgeon for pruning neural network architecture applied to multivariate calibration , 1998 .

[21]  Saso Dzeroski,et al.  Stepwise Induction of Multi-target Model Trees , 2007, ECML.

[22]  Concha Bielza,et al.  A survey on multi‐output regression , 2015, WIREs Data Mining Knowl. Discov..

[23]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[24]  J. Friedman,et al.  Predicting Multivariate Responses in Multiple Linear Regression , 1997 .

[25]  Jack Y. Yang,et al.  Combining support vector regression with feature selection for multivariate calibration , 2009, Neural Computing and Applications.

[26]  Gerard J. P. van Westen,et al.  Significantly Improved HIV Inhibitor Efficacy Prediction Employing Proteochemometric Models Generated From Antivirogram Data , 2013, PLoS Comput. Biol..

[27]  K S Pang,et al.  Disposition of enalapril in the perfused rat intestine-liver preparation: absorption, metabolism and first-pass effect. , 1985, The Journal of pharmacology and experimental therapeutics.

[28]  S. Džeroski,et al.  Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition , 2009 .

[29]  T. Lane,et al.  A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction , 2009, TCBB.

[30]  Jian-Hui Jiang,et al.  Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: Application in QSAR studies of bioactivity of organic compounds , 2007, J. Comput. Chem..

[31]  D. Drossman,et al.  Irritable bowel syndrome: a technical review for practice guideline development. , 1997, Gastroenterology.

[32]  R Jones,et al.  Guidelines on the irritable bowel syndrome: mechanisms and practical management , 2007, Gut.

[33]  Elias Masry,et al.  MULTIVARIATE LOCAL POLYNOMIAL REGRESSION FOR TIME SERIES:UNIFORM STRONG CONSISTENCY AND RATES , 1996 .

[34]  Charles A. Micchelli,et al.  Learning Multiple Tasks with Kernel Methods , 2005, J. Mach. Learn. Res..

[35]  J. Zidek,et al.  Adaptive Multivariate Ridge Regression , 1980 .

[36]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[37]  A. Belousov,et al.  Applicational aspects of support vector machines , 2002 .

[38]  Charles A. Micchelli,et al.  On Learning Vector-Valued Functions , 2005, Neural Computation.