Computational and Mathematical Methods in Medicine

Computational and Mathematical Methods in Medicine / 2016 / Article

Research Article | Open Access

Volume 2016 |Article ID 6794916 | https://doi.org/10.1155/2016/6794916

Selen Yılmaz Isıkhan, Erdem Karabulut, Celal Reha Alpar, "Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data", Computational and Mathematical Methods in Medicine, vol. 2016, Article ID 6794916, 9 pages, 2016. https://doi.org/10.1155/2016/6794916

Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data

Academic Editor: Chung-Min Liao
Received29 Jul 2016
Revised18 Nov 2016
Accepted27 Nov 2016
Published20 Dec 2016

Abstract

Background/Aim. Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods. Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results. The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of as a cutoff point for RT bagging to outperform a single RT.

1. Introduction

Microarray technology can simultaneously measure the expression levels of thousands of genes in a biological sample. In genome experiments, researchers frequently encounter high-dimensional data with a small sample size [1]. Regression and classification applications created according to classical statistical methods work based on assumptions known as probability distribution models. These assumptions are difficult to satisfy for high-throughput datasets. Therefore, when the probability assumption is unknown, the use of distribution-independent methods is required. Classical statistical methods, such as logistic regression and linear regression analysis, have difficulty in explaining thousands of genes of a small number of individuals. Data mining methods on the other hand conclude the analyses correctly almost requiring no assumption. These methods are useful when there are many explanatory variables available, and they can even be used to examine nonlinear data structures and do not need any particular distribution of the response variable. Various data mining applications, which provide successful results for datasets containing a small number of observations in the high-dimensional space especially in biological applications, have been widely used. Support vector machines (SVMs), decision trees, and boosted trees have recently been used on such data types as alternative tools [28]. However, most of them have compared bagging and boosting methods for classification algorithms. For example, Martinez and Suarez examined the effects of the sampling ratio on the properties of the bagging ensembles for classification trees [9]. They analyse bagging in 30 datasets using different sampling ratios from 2% to 120%. Their results demonstrated that using smaller training samples could be useful to improve the generalization performance of the ensemble for several datasets. There is no such study showing how a small sample size affects the regression performances of bagging and boosting.

The aim of this study is to evaluate clinical-dose estimation by genome data (with various sample size) using several data mining techniques. For example, in the International Warfarin Pharmacogenetics Consortium report, “Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data” whose authors Klein, Altman, and Eriksson became committee members in 2009, therapeutic warfarin dose estimation was obtained using genetic factors, such as gene expression values, and some environmental factors as independent variables [10]. Similarly, various quantitative clinical or chemometrical components were predicted in this study using only gene expression sequences. For this purpose, real datasets containing both gene sequences and clinical-dose measurements related to humans, rat types, and yeast species were used. To avoid making assumptions about the type of relation between the dose values and DNA gene expression data, to cope with high-dimensional or nonlinear data structures, data mining methods such as regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were applied and compared in this study [6, 8].

2. Materials and Methods

2.1. Preparation of Genetic Data and Feature Selection

Eleven real dose-gene expression datasets were used as real datasets to implement the prediction process. For this purpose, we searched the gene sequences in the GEO database and selected datasets containing a numerical measurement such as dose or concentration. DNA expression profiles of humans, rat types, and yeast species were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO). These datasets were used in different studies that examine expression profiles of gene sequences at varying dose or concentration levels (e.g., copper concentration for human and glucose concentration for rat) for different living species. Two of these belong to yeast, seven belong to rats, and the remaining two contain human gene sequences. Our main criterion in the selection of these datasets is that they have both a gene sequence and a numerical response value (e.g., dose). Therefore, a common characteristic of the datasets is that each has gene expression levels separately constituting thousands of columns, and each consists of a quantitative clinical measurement considered as response variable. In addition, sample size of the datasets ranges from 15 to 98. Detailed information about datasets are shown in Table 1.


Data/IDOrganism typeResponse variableNumber of observationsTotal genesSelected genes

GSE1938YeastMannose concentration1587142
GSE12817RatGlucose concentration16246065
GSE3078RatHydrogen peroxide20141026
GSE10748RatD-serine dose24246067
GSE15457RatHypochlorous acid25272764
GSE8982YeastAlpha mating factor pheromone3361919
GSE14954HumanLDL-cholesterol concentrations401640110
GSE9751RatChlorpyrifos (CPF) dose441342911
GSE7955RatDeltamethrin dose482460614
GSE2409RatAnticancer drug dose69798716
GSE9539HumanCopper sulfate981821121

Some datasets had transformed counts. The transformation was applied to nontransformed gene expression datasets. This normalization process adjusts the individual hybridization intensities to balance them appropriately to ensure that meaningful biological comparisons can be made [11]. In the next step, genotypes containing excessively many missing observations were excluded from the datasets.

Feature selection is another important step in selecting a small number of predictor variables having significant effects on the response variable, especially on high-dimensional gene data. Since the number of features is much larger than the sample size, classical methods such as ordinary least squares fail to fit the linear regression model. In addition, it is assumed that only a few of the features (genes) are actually associated with the response values. Hence, we must first identify the genes that are responsible for the dose of a clinical measurement to reduce the number of effective genes [12]. An iterative sure independence screening (SIS) feature selection method was conducted for this. When the model assumptions are not satisfied, SIS can miss important predictors. To overcome this problem, Fan and Lv [13] proposed iterative SIS to enhance methodological power. Iterative SIS could detect the combination effects of some marginally weak genes with the response variable by conducting SIS and lasso regression interactively. The basis is to apply large scale variable screening iteratively followed by moderate-scale careful variable selection [12, 13].

2.2. Regression Methods

First, we considered clinical-dose values () as response variable and gene expression profiles as predictors (). Then, we identified small sets of predictor genes using iterative SIS method and built regression models (), where is the predicted dose, is gene expression profiles, and β is a coefficient vector. These models were applied to predict clinical-dose values using only predictor gene expression data. For dose prediction, we used 6 regression models (SVR-linear, SVR-polynomial, SVR bagging, RT, RT bagging, and RT boosting). Training data (70% of data) was utilized to determine the optimal value of parameters and fit the models. Finally, model performance was estimated using the remaining (testing) data.

2.2.1. Support Vector Regression (SVR)

Unlike the classical regression methods, SVR focuses on minimizing the generalization error instead of minimizing the observed training error. Vapnik has defined an -insensitive loss function (-SVR) to generalize support vector algorithms to apply to regression situations. The purpose of -SVR is to find the function with a maximum deviation of from the target values for all training datasets. The linear function condition of is defined as follows:where is the flatness of the function and the following optimization problem is solved to minimize the Euclidean form.

Objective is

Equation (3) is the experimental error measured with the -insensitive loss function [14]. As long as the errors are less than , they are ignored (considered zero). The regularization parameter defines the balance between the flatness of and the tolerated [14, 15]. Defining the kernel function, provides the objective function for the nonlinear solution:where and are Lagrange multipliers.

2.2.2. Regression Trees (RTs)

The purpose of RT analysis is to explain a continuous response variable by explanatory variable vector , which can be a random mixture of quantitative, ordinal, and nominal variables [16]. A tree is developed by considering a root node containing primarily all of the observations. Observations in this node are sent to one of the two subnodes (left and right) using a split point on a single explanatory variable. The binary split process is applied repeatedly to its output until it reaches some stop criterion.

The response variable in each area for a split of as the th area is modelled as a constant [17]:

Here, is the indicator function and is the prediction value for . Using the minimization criterion of the sum of squared errors reveals that the best value of is the average of values in the region :

In the RT formation step, two regions are created by applying a greedy algorithm for each explanatory variable and split point . Then, the first splitting variable and split point are obtained by solving the the following [17]:

2.2.3. Ensemble Methods

Ensemble methods aim to improve the estimation performance of a given statistical learning or model-building technique. The basic principle of ensemble methods is to create a linear combination of model fitting methods instead of using only a single method. Bagging and boosting are two examples of aggregation methods used to increase the accuracy of classifiers or estimators. Yang et al. [18] proposed that ensemble methods such as bagging and boosting are effective in dealing with classification in high-throughput biological experiments. Therefore, such methods have been preferred recently owing to their special advantages in dealing with small sample size, high dimensionality, and complex data structures [8].

Classical bagging yields more robust and accurate models using bootstrap resamplings of the training data [18, 19]. The bagging procedure consists of two steps. First, bootstrap samples are drawn from the original data to form training sets, from which multiple models are obtained. Then, these models are combined to make predictions [19]. It has been shown [20] that bagging is especially suitable for unstable models such as tree-structured models.

Boosting was first introduced in 1990 by Freund and in 1995 by Schapire to improve classification [20]. As in bagging, the estimators that create the ensemble are obtained by resampling data and are then combined with the majority vote in the boosting method. Resampling a training set in bagging does not depend on the performance of the previous estimators. However, in boosting, the sampling probabilities of the samples that have the most different estimation values compared to the observed values for the regression predictors are adjusted to be higher as members of the training set for the second step. The estimations are combined using the weighted median by assigning greater weights to the predictors that are more reliably related to the predictions [21]. Boosting regression trees (BRTs) provide a method that aims to improve the performance of a single model by fitting multiple models and combining them for estimation. BRT uses two algorithms: RTs and boosting [2023]. Hastie et al. (2001), who first established the connection between boosting and optimization, recommend the gradient-boosting machine (GBM) [17]. GBM is based on the AdaBoost algorithm, a derivative decrease algorithm on a loss function [24, 25]. Boosting minimizes this loss function by modelling the residuals at every step.

2.3. Simulation Data

In order to ensure the effect of the sample size on the used ensemble methods, simulation data have been derived according to selected genes utilizing iterative SIS method. The gene expression-dose dataset having access number GSE2409 with a sample size of has been used for this purpose. This dataset has been randomly selected to be an example. The number of the selected genes with iterative SIS method then has been reduced to 8 important genes by considering multicollinearity and the significance of the regression coefficients. The simulation data have been derived from the multivariate normal distribution by using the correlation matrix between gene expression measurements and dose values. In order to capture the true relation structure in the dataset, the zero mean vector and the real correlation matrix were used [26]. Data were obtained through the function of MASS packages in the R software. The first column of the obtained data is the standardized scores of the dose values considered as the response variable. After model fitting step, these values were converted into the original dose scores using the mean and standard deviation of raw dose values as follows:where is the standardized score, is the raw dose score, and and are the mean and the standard deviation of the raw dose scores, respectively. These data generation steps were repeated 500 times.

2.4. Parameter Setting

Function types whose model performance was evaluated for SVR were linear, polynomial, radial basis, and sigmoid. However, only the solutions of linear function providing the smallest RMSE estimation were considered in the results section.

Generalization performance of SVR depends on setting some hyperparameters (cost), (epsilon) and (Gamma, specific to kernel function) well [14, 27, 28]. Optimization of and for linear function was achieved while the optimization of all the three parameters for other nonlinear kernel functions was achieved simultaneously using tune() function in . The valid search interval of (cost parameter) in tune.svm() command was determined between and 102. On the other hand, the search interval of (gamma parameter) was determined between 10−3 and 102 (that is, 6 search points). Searches for 0.05, 0.10, and 0.20 values of in the related ranges of and parameters were conducted; then, the combination of parameters which gives the smallest prediction error (MSE) for 10-fold cross-validation was identified. Similarly, minimum number of observations in a node, complexity parameter, and node number with subdivision (interaction level) parameters for RT were searched using cross-validation. Optimized parameter values were fixed for the method versions within the same cycle.

2.5. Statistical Analysis

Statistical analyses were performed in R 3.1.2 programming language. Each dataset was divided into training (70%) and test (30%: evaluation) sets to avoid overfitting. When creating the regression models, the R packages e1071, rpart, ipred, and gbm were used for SVR, RTs, RT bagging, and GBM, respectively. R code was written to build a SVR-bagging model. The average performances of 100 training/test divisions and 50 bagging repetitions were considered in the analyses.

The prediction performances of related models were compared with the root mean squared error (RMSE), mean absolute deviation (MAD), and coefficient of determination () measures, defined as follows:where and are the observed and predicted values of the response variable, is the observation number, and SSR is the sum of squared regression, SSE is the sum of squared errors, and SST is the sum of squared of total variation of .

3. Results

3.1. Real Data Results

First, regression-based iterative SIS feature selection was applied to the previously prepared datasets substantially reducing the number of irrelevant genes from the raw datasets. After feature selection, only a small percentage of selected genes in all of the datasets remained. Detailed information and the number of selected important genes related to the datasets can be found in Table 1.

Thereafter, training-test performances with 100 random splits of single, bagging, and boosting models of the prediction methods were evaluated for the datasets. Prediction results of the SVR, RT, SVR bagging, RT bagging, and GBM learning techniques for each dataset are also shown as average estimates in Table 2. Except for the GSE10748 dataset (), values were generally found to be sufficient. Overall, the best prediction performance was obtained by SVR in nine of 11 datasets. In nine datasets, after SVR, the second best accuracy performance was provided by GBM. Average values changed from 0.37 to 0.97.


DatasetSVR (lin)SVR (pol)SVR baggingRTRT baggingGBM

GSE19380.8740.9410.8380.7240.943
GSE128170.9250.9630.7920.8470.945
GSE30780.5830.5380.7590.739
GSE107480.3670.2890.254
GSE154570.4070.7690.720
GSE89820.6860.5740.5930.6160.720
GSE149540.6530.7100.4260.5840.781
GSE97510.3630.4570.135
GSE79550.4660.4430.1980.3000.325
GSE24090.2630.7140.4400.5380.644
GSE95390.7000.8230.5520.6640.833

Bold indicates the best performance; : increase in ; : decrease in compared to single performance. and : significant at 0.001 and 0.05 level, respectively. SVR (lin), SVR (pol), and RT are single performances, while the others are ensemble performances of the methods.

For the GSE1938 dataset, the two genes that most affected the mannose concentration were CWP1 and NCA3. These two genotypes together best explained the mannose concentration, approximately 96%. In another yeast dataset with access number GSE8982, a total of nine important genes were determined to predict the concentrations of the alpha mating factor pheromone. Selected genes included DIG2, GYP8, YHR097C, ACM1, LCB3, PRM4, PRM3, FYV10, and UTP7, and all had the best explained variation, approximately 74%. In the analysis of the GSE12817 dataset, important genes such as Adm, Aldob, Pkm2, Crem, and nod3l were identified. Their explanation success of glucose concentration was found to be approximately 97%. Similarly, in the rat dataset with access number GSE2409, the selected 16 genes were RGD1309228, Ccdc21, Setd5, RGD1303130, Vps26a, Sept2, BF396256, Adprhl2, BE109616, Phf3, Tfb2m, Lyplal1, Arg1, Ndufs2, Tmem208, and Nsun2. Together, they had the best values for anticancer drug dose, approximately 81%. When the two human datasets (GSE14954 and GSE9539) were considered, their values were found to be sufficiently high. The best average estimates were 80.4% and 86.7%, respectively.

Twenty-two genotypes affecting copper concentrations were determined as MCL1, DDX28, MRPS26, RPUSD2, DDIT3, PELO, SLC30A1, C20orf111, SOCS3, SOX9, DNAJB4, NXF1, MAF1, MAFG, AMOTL2, ADM, ZFAND2A, ZFAND5, DUSP1, TBCC, HPS6, and INTS5.

The distribution ranges of the RMSE estimates generated with 100 repetitions for the method performances are presented in Figures 1 and 2 and categorized according to sample size. The plots obtained for five datasets in the condition and six datasets in the condition are shown in Figures 1 and 2, respectively. It is obvious that the bagging RT performance had no advantage over RT consistently for all datasets (Table 2 and Figure 1). However, in datasets with (Figure 2), bagging RT showed better performance compared to single RT in terms of RMSE and its standard error. On the contrary, this result is not valid for SVR performance. In both plots, SVR bagging did not outperform single performance for any dataset.

Within the SVR learning technique, a linear kernel function provided lower RMSE estimates than a polynomial one. Except for the dataset with ID number GSE7955, the polynomial model did not improve the average RMSE value (Figures 1 and 2).

3.2. Simulated Data Results

The selected 8 genes for GSE2409 dataset were obtained as Alg1, BF282239, AW920082, Ptpn12, BE103975, Prickle2, Nsf, and Phf3. In addition, the significance values of regression coefficients regarding them were found as , . Standard dose scores obtained based on (8) for selected database (GSE2409) were converted to raw dose estimations using dose mean (13.60) and its standard deviation (9.59). The results estimated by 500 repetitions of the simulation data are given visually in Figure 3. When these results are examined, it can be seen that the training set dimension of is a cutoff point for the bagging method.

When the training set size is , the bagging performance (pink dotted line) and single performance (blue straight line) of RT are very close; however, when , error predictions of bagging and boosting steadily decrease, but boosting gives a better performance compared to the others. These results are consistent with those of the original datasets.

4. Conclusions

In this study, the performances of two commonly used machine learning techniques, SVR, and RT, and their ensembles in predicting various dose values were compared on high- dimensional microarray datasets. A total of 11 real datasets obtained from the GEO database were used. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. For instance, the study that analysed rat pancreatic islets cultured for 18 h in 2, 5, 10, or 30 mM glucose concentrations of raw data with accession number GSE12817 identified the 40 genes most affected by glucose [29]. These included 16 upregulated genes, 19 downregulated genes,and five genes with a V-shaped profile. Aldob, Txnip, Crem, Adm, and Fos were found among the 16 most upregulated genes known to be strongly induced by high glucose. Similarly, five genotypes were determined in our analysis as having the greatest effect on glucose concentration. These were the Adm, Aldob, Pkm2, Crem, and nod3l genotypes. Moreover, the explanation success of glucose concentration by these five genotypes was found to be approximately 97%. The GSE14954 dataset belonged to 20 male and 20 female participants with ages between 45 and 60 years and body mass indices ≥25 kg/m2. All participants received two different diets and then their LDL-cholesterol concentrations and insulin sensitivity levels were compared [30]. It was found that a saturated fatty acid diet resulted in changed expression of 1523 genes, whereas a monounsaturated fatty acid diet resulted in changed expression of 592 genes. In our study, we demonstrated approximately 80% prediction success of LDL-cholesterol concentrations using only genetic information of all the participants. The genotypes most affecting LDL-cholesterol concentrations were identified as LOC100289611, PKDCC, DCTD, LASS6, KDSR, UBXN6, PDE8B, DMWD, SPATA20, and RGS7BP. For the GSE9539 dataset, human HepG2 cells were treated with 100, 200, 400, or 600 μM copper sulfate. A related study [31] identified a total of 2257 differentially expressed genes by fold change. Of these, the upregulated genes were found to be HSPs, BAG3, SOCS3, GADD45G, GCLM, VLDLR, CYR61, DUSP1, DUSP5, FOS, EGR1, MAFB, NR4A1, PROP1, TGFB1, DNAJB1, ADM, and DDIT3. On the other hand, MCL1, IFRD1, JAG1, MAFB, CAP1, and FGD6 genotypes were among the downregulated genes. In our analysis, 22 genotypes affecting the copper concentrations were determined. Seven of these, MCL1, DDIT3, SOCS3, DNAJB4, MAFG, ADM, and DUSP1, were found to be similar to those reported by Song et al. [31]. An important finding related to this dataset was that it had considerable predictive success (). Harrill et al. [32] used two penalized regression methods to detect dose-dependent changes in the gene transcription of rat frontal cortex. For deltamethrin and permethrin doses, 95 of 109 (87.1%) and 53 of 89 (59.5%) probe sets passed the ANOVA significance threshold. Egr1, c-fos, Gpdl, Fkbp51, Hsp27, Camklg, Bdnf, Rassf5, and LOC689415 were found among the genes upregulated by deltamethrin and permethrin. On the other hand, Ddc, Slc39a8, Pldl, LOC682926, Slit2, Klf4, and Bves were found among the genes downregulated by deltamethrin and permethrin. In this study, we identified 14 genes that can most affect dose values. Among them, c-fos, LOC689415, and Bdnf were found to be similar to those reported by Harrill et al. [32]. However, the deltamethrin dose success of these 14 genotypes was about 55%.

When the prediction performances were compared, the fact that SVR performs better than other methods closely resembles the results of some other studies [6, 14, 33]. While the lowest dose was obtained as 0.367, the highest was 0.978.

An important finding of this study is that the dataset size of was identified as a cutoff point for RT bagging. When RMSE estimates from RT to RT bagging were examined with changing sample size, it was seen that bagging did not improve single RT performance up to the datasets with sample size of 25. However, the estimates of RT bagging gained consistency from the dataset with ID code GSE8982 (), and this improvement became more visible with an increase in the number of observations. Consequently, GBM provided more optimistic performance in datasets with compared to RT bagging. It was also found that the results obtained from the simulated data were consistent with the application of real gene data. However, this condition is not valid for SVR performance because SVR bagging did not outperform single SVR. When SVR performance was taken into consideration, in either condition ( or ), bagging could not provide a significant reduction in the RMSE average or variance. This finding is consistent with the results of some other studies in the literature [22, 34].

Furthermore, frequent utilization of iterative SIS feature selection for genomic data was supported in this study, because it enables the selection of a smaller number of genes to contribute to regression estimation from thousands of raw genes.

Disclosure

A part of this study was presented orally at the 17th National Conference of Biostatistics Cyprus, November 2015.

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. M. R. Segal, K. D. Dahlquist, and B. R. Conklin, “Regression approaches for microarray data analysis,” Journal of Computational Biology, vol. 10, no. 6, pp. 961–980, 2003. View at: Publisher Site | Google Scholar
  2. A. Smola and V. N. Vapnik, “Support vector regression machines,” in Advances in Neural Information Processing Systems, vol. 9, pp. 155–161, MIT Press, 1997. View at: Google Scholar
  3. M. P. S. Brown, W. N. Grundy, D. Lin et al., “Knowledge-based analysis of microarray gene expression data by using support vector machines,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 1, pp. 262–267, 2000. View at: Publisher Site | Google Scholar
  4. N. Ye, The Handbook of Data Mining, Lawrence Erlbaum Associates Publishers, London, UK, 1st edition, 2003.
  5. A.-L. Boulesteix, C. Strobl, T. Augustin, and M. Daumer, “Evaluating microarray-based classifiers: an overview,” Cancer Informatics, vol. 6, pp. 77–97, 2008. View at: Google Scholar
  6. J. C. Abrahantes, Z. Shkedy, and G. Molenberghs, “Alternative methods to evaluate trial level surrogacy,” Clinical Trials, vol. 5, no. 3, pp. 194–208, 2008. View at: Publisher Site | Google Scholar
  7. A. Cutler, D. R. Cutler, and J. R. Stevens, “Tree-based methods,” in High-Dimensional Data Analysis in Cancer Research, X. Li and R. Xu, Eds., Springer, New York, NY, USA, 1st edition, 2009. View at: Google Scholar
  8. E. Cosgun and E. Karaagaoglu, “Veri Madenciliği Yöntemleriyle Mikrodizilim Gen İfade Analizi,” Hacettepe Tıp Dergisi, vol. 42, pp. 180–189, 2011. View at: Google Scholar
  9. G. Martínez-Muñoz and A. Suárez, “Out-of-bag estimation of the optimal sample size in bagging,” Pattern Recognition, vol. 43, no. 1, pp. 143–152, 2010. View at: Publisher Site | Google Scholar | Zentralblatt MATH
  10. T. E. Klein, R. B. Altman, N. Eriksson et al., “Estimation of the warfarin dose with clinical and pharmacogenetic data,” The New England Journal of Medicine, vol. 360, no. 8, pp. 753–764, 2009. View at: Publisher Site | Google Scholar
  11. J. Quackenbush, “Microarray data normalization and transformation,” Nature Genetics, vol. 32, no. 5, pp. 496–501, 2002. View at: Publisher Site | Google Scholar
  12. Y. Fang, Y. Qin, N. Zhang, J. Wang, H. Wang, and X. Zheng, “DISIS: prediction of drug response through an iterative sure independence screening,” PLoS ONE, vol. 10, no. 3, Article ID e0120408, 2015. View at: Publisher Site | Google Scholar
  13. J. Fan and J. Lv, “Sure independence screening for ultrahigh dimensional feature space,” Journal of the Royal Statistical Society, Series B: Statistical Methodology, vol. 70, no. 5, pp. 849–911, 2008. View at: Publisher Site | Google Scholar | MathSciNet
  14. V. N. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988–999, 1999. View at: Publisher Site | Google Scholar
  15. G.-Z. Li, H.-H. Meng, M. Q. Yang, and J. Y. Yang, “Combining support vector regression with feature selection for multivariate calibration,” Neural Computing and Applications, vol. 18, no. 7, pp. 813–820, 2009. View at: Publisher Site | Google Scholar
  16. T. Grubinger, C. Kobel, and K.-P. Pfeiffer, “Regression tree construction by bootstrap: model search for DRG-systems applied to Austrian health-data,” BMC Medical Informatics and Decision Making, vol. 10, no. 1, article 9, 2010. View at: Publisher Site | Google Scholar
  17. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer Series in Statistics, Springer, New York, NY, USA, 2001. View at: Publisher Site | MathSciNet
  18. P. Yang, Y. H. Yang, B. B. Zhou, and A. Y. Zomaya, “A review of ensemble methods in bioinformatics,” Current Bioinformatics, vol. 5, no. 4, pp. 296–308, 2010. View at: Publisher Site | Google Scholar
  19. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996. View at: Google Scholar
  20. P. B{\"u}hlmann and T. Hothorn, “Boosting algorithms: regularization, prediction and model fitting,” Statistical Science, vol. 22, no. 4, pp. 477–505, 2007. View at: Publisher Site | Google Scholar | MathSciNet
  21. G. Ridgeway, “The state of boosting,” Computing Science and Statistics, vol. 31, pp. 172–181, 1999. View at: Google Scholar
  22. H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” in Advances in Neural Information Processing Systems, vol. 9, pp. 155–161, MIT Press, 1997. View at: Google Scholar
  23. L. Breiman, “Using adaptive bagging to debias regressions,” Tech. Rep. 547, University of California-Department of Statistics, Berkeley, Calif, USA, 1999. View at: Google Scholar
  24. G. Ridgeway, “gbm: Generalized boosted regression models,” R package version 1.6-3, 2007. View at: Google Scholar
  25. J. H. Friedman, “Stochastic gradient boosting,” Computational Statistics & Data Analysis, vol. 38, no. 4, pp. 367–378, 2002. View at: Publisher Site | Google Scholar
  26. A. Burton, D. G. Altman, P. Royston, and R. L. Holder, “The design of simulation studies in medical statistics,” Statistics in Medicine, vol. 25, no. 24, pp. 4279–4292, 2006. View at: Publisher Site | Google Scholar | MathSciNet
  27. W. Jiang and R. Simon, “A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification,” Statistics in Medicine, vol. 26, no. 29, pp. 5320–5334, 2007. View at: Publisher Site | Google Scholar | MathSciNet
  28. P. Refaeilzadeh, L. Tang, and H. Liu, “Cross validation,” in Encyclopedia of Database Systems (EDBS), L. Liu and M. T. Özsu, Eds., pp. 532–538, Springer, New York, NY, USA, 1st edition, 2009. View at: Google Scholar
  29. M. Bensellam, L. Van Lommel, L. Overbergh, F. C. Schuit, and J. C. Jonas, “Cluster analysis of rat pancreatic islet gene mRNA levels after culture in low-, intermediate- and high-glucose concentrations,” Diabetologia, vol. 52, no. 3, pp. 463–476, 2009. View at: Publisher Site | Google Scholar
  30. S. J. Van Dijk, E. J. M. Feskens, M. B. Bos et al., “A saturated fatty acid-rich diet induces an obesity-linked proinflammatory gene expression profile in adipose tissue of subjects at risk of metabolic syndrome,” American Journal of Clinical Nutrition, vol. 90, no. 6, pp. 1656–1664, 2009. View at: Publisher Site | Google Scholar
  31. M. O. Song, J. Li, and J. H. Freedman, “Physiological and toxicological transcriptome changes in HepG2 cells exposed to copper,” Physiological Genomics, vol. 38, no. 3, pp. 386–401, 2009. View at: Publisher Site | Google Scholar
  32. J. A. Harrill, Z. Li, F. A. Wright et al., “Transcriptional response of rat frontal cortex following acute in vivo exposure to the pyrethroid insecticides permethrin and deltamethrin,” BMC Genomics, vol. 9, article no. 546, 2008. View at: Publisher Site | Google Scholar
  33. K. A. McQuisten and A. S. Peek, “Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs,” PLOS ONE, vol. 4, no. 10, Article ID e7522, 2009. View at: Publisher Site | Google Scholar
  34. P. L. Braga, A. L. I. Oliveira, G. H. T. Ribeiro, and S. R. L. Meira, “Bagging predictors for estimation of software project effort,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '07), pp. 1595–1600, Orlando, Fla, USA, August 2007. View at: Publisher Site | Google Scholar

Copyright © 2016 Selen Yılmaz Isıkhan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views1060
Downloads284
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.