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.

A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data

“…Our results are not in agreement with those of Li et al (2017), who used the multi-trait regressor stacking method for time series prediction of drug efficacy and found that this method was considerably superior with regard to univariate analysis in most datasets they evaluated. The results of the present study are in agreement with those of Melki et al (2017), who evaluated the multi-trait regressor stacking method under the support vector regression framework and found the trick of expanding the predictors in the second stage with the predictions of the target traits in the first stage in order to improve prediction accuracy.…”

Survey on Multi-Output Learning

“…Real-valued Vector River Quality Prediction [22] Ecology Natural Gas Demand Forecasting [23] Energy Meteorology Drug Efficacy Prediction [24] Medicine…”

“…Multi-target Regression Independent Real-valued Vector River Quality Prediction [22] Ecology Natural Gas Demand Forecasting [23] Energy Meteorology Drug Efficacy Prediction [24] Medicine Label Distribution Learning Distribution Head Pose Estimation [25] Computer Vision Facial Age Estimation [26] Computer Vision Text Mining [27] Data Mining…”

Antitumor component recognition from the Aconiti Lateralis Radix Praeparata and Glycyrrhizae Radix et Rhizoma herb pair extract by chemometrics and mean impact value

“…MIVs of the 30 common peaks from the 31 different samples were acquired as described in Section 2.8 and the 30 peaks were then ranked by their absolute MIVs, as shown in Table 4. A higher absolute MIV means that the corresponding component contributes more to the inhibition effect of the Fuzi-Gancao extract on HeLa cells, and therefore, the top 8 components (peaks 17,25,22,13,23,28,5,7) are temporarily deemed to possess good antitumor activity. The top 8 components are liquiritin, 14-benzoyldeoxyaconine, benzoylhypaconine, 14-acetyltalatizamine, formononetin, 24-hydroxy glycyrrhetinic acid, senbusine A/B and aconine.…”

“…Some achievements have been made by this strategy, demonstrating its effectiveness. [3][4][5] To date, various algorithms have been applied to CAR model construction. However, since HMs contain a wide variety of compounds and complicated mutual interactions exist among these components, the commonly used linear models like multiple linear regression analysis (MLR) usually do not t the practical situation very well.…”