title: A novel multi-target regression framework for time-series prediction of drug efficacy.
abstract: 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.
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China.
authors.forename: Haiqing
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Institute of Chinese Materia Medica, China Academy of Chinese Medical Science, Beijing, 100700, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China.
authors.forename: Yumeng
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China.
authors.forename: Guo-Zheng
authors.initials: GZ
authors.lastname: Li
authors.affiliation: Institute of Chinese Materia Medica, China Academy of Chinese Medical Science, Beijing, 100700, China.
authors.forename: Xiaoxin
authors.initials: X
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D008516:Medicine, Chinese Traditional; D016571:Neural Networks, Computer; D012044:Regression Analysis