Prediction of drug synergy in cancer using ensemble ...

Modern Physics Letters B 1850132 (12 pages) c World Scientific Publishing Company

DOI: 10.1142/S0217984918501324

Mod. Phys. Lett. B Downloaded from www.worldscientific.com by THE UNIVERSITY OF CHICAGO on 04/10/18. For personal use only.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Harpreet Singh∗,‡ , Prashant Singh Rana∗ and Urvinder Singh† ∗ Computer

Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India † Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India ‡ [email protected] Received 24 October 2017 Revised 1 February 2018 Accepted 21 February 2018 Published 10 April 2018 Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug–drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation. Keywords: Drug synergy score; ANFIS; ensembling; DENFIS; random forest.

1. Introduction Drug synergy or combination of different drugs is extensively utilized in treating the most dreadful diseases, such as cancer.1 The main objective is to achieve synergistic therapeutic effect, dose and toxicity reduction and to decrease the induction of 1850132-1


H. Singh, P. S. Rana & U. Singh

drug resistance.2 Recent advancements in the study of cancer have shown that the disease cannot be explored only with the help of genetic mutations within the cancer cells.3 Instead, tumors should be considered as compound tissues in which cancer cells grow and interconnect with neighboring micro-environment to endorse their survival and distribution,4 though, remedies that target specific signaling proteins have been effective for cancer treatment.5 Majority of classical chemo drugs kill cells in the body that produce and divide rapidly.6 Cancer cells divide at a faster speed. Therefore, these drugs work efficiently against them.7 However, it can also harm other cells in the body that share rapidly. Therefore, it leads specific side effects to human beings.8 Throughout, the discovery process, estimating the drug amalgamation consequence in vitro profoundly depends on the examination. But, the testing of entire in vitro set in cancer is not possible because of substantial combinatorial search space.9 The computational estimation may help in recognizing probable drug mixture effect at a molecular level, such as hybridization of molecular and pharmacological data.10 A regression-based estimate of drug–drug interactions (DDIs) by combining drug phenotypic, therapeutic, chemical and genomic properties and large-scale pharmacogenomic screens of cancer cell lines is made.11 These techniques are efficient to estimate unknown DDIs.12 Therefore, any computational approach to either guide readily testable candidates or reliably predict the effect of drug groupings would be expectable.13 Figure 1 shows the diagrammatic flow of the machine learning-based drug synergy score detection. Initially, medical data will be loaded into the system’s workspace and partition of data will be done between training and testing data.14 Then, machine learning-based techniques come in action to train a drug synergy model by considering training data.15 In the end, the trained model is utilized to predict the synergy score from testing data.16 Taghizadeh et al. utilized an adaptive neuro-fuzzy inference system (ANFIS) to predict the surgery data. In this approach, attributes were reduced by considering a univariant analysis to make the machine learning model more efficient.17 Chahkandi et al. designed a gradient-based iterative learning technique for parameter tuning of neuro-fuzzy-based machine learning model. The premise and following attributes of neuro-fuzzy were taken randomly and then optimized using an iterative technique. This technique utilizes the first-order partial derivative of the predicted class concerning the structure attributes.18 It provides better accuracy compared to standard ANFIS and neuro-fuzzy model.16 Keeley et al. studied that to obtain an efficient fuzzy classifier, substantial efforts are required to optimize fuzzy sets.19 But, these models do not focus on the method in which membership functions are integrated within fuzzy rules.20 Therefore, standard fuzzy techniques have no control over how powerfully or inadequately the inference is implemented within these regulations.21 A genetic-based fuzzy inference system was applied to improve the performance of fuzzy classifier system.22 Each fuzzy system is developed by considering well-established decision tree

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Fig. 1.

Flowchart of the proposed technique.

techniques such as C4.5 and CHAID.23 The proposed method provides efficient results compared to existing techniques.23 Therefore, the main issue associated with the existing machine learning techniques for drug synergy prediction suffers from low accuracy rate.24 Shuang and Antony proved that neural network-based ensembles are effective approaches to enhance the accuracy of neural network-based machine learning techniques. Integrating a set of neural network techniques whose error distributions are diverse can achieve better prediction outcomes than standard neural network classifiers.25 Frederic and Max demonstrated that an ensemble-based machine learning techniques improve the performance by integrating multiple models and their respective outcomes.26 Yigit and Ufuk utilize six well-known machine learning techniques, such as decision tree, random forest, Bayesian network,27 Nave Bayes, support vector machine and K ∗ , to design an ensemble-based machine learning model. The voting technique is utilized to aggregate the output of other models.28 Akin studied that the ensemble-based machine learning technique integrated different machine learning models that make errors in different fragments of analyzed data.29 Therefore, we have proposed a novel ensembling-based drug synergy prediction technique that overcomes the above discussed issues.

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Contributions: Following are our main contributions in this research paper: (i) Initially, we have implemented nine well-known machine learning techniques on the drug synergy dataset. Among them, four techniques are selected with high accuracy rate. (ii) After that, these techniques such as Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-NetworkBased Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) are used to propose an ensembling-based drug synergy prediction technique. (iii) To validate the proposed technique, comparisons have been drawn between proposed and existing techniques by considering various sizes of training data, i.e. 40%, 50%, 60%, etc. The structure of the paper is as follows. The proposed ensemble-based machine learning technique is described in Sec. 2. Experimental results and comparative analysis are described in Secs. 3 and 4. The concluding remarks are presented in Sec. 5. 2. Proposed Technique In this section, proposed technique is described using mathematical formulation. Initially, ANFIS and Random forest-based machine learning models have been explained. Then, a proposed ensemble-based model is described. 2.1. ANFIS The fuzzy and neural networks come up with certain pros and cons. Therefore, researchers have merged them to utilize the features of both techniques. The integrated model is called Adaptive neuro-fuzzy system (ANFIS).30 It contains multiple layered feed-forward neural networks. First-order fuzzy logic is utilized as a rule-based with two inputs Vα and Vβ with a function response f1 , f2 , . . . , fn . The rule-based can be defined as follows: Rule 1: If Vα is A1 and Vβ is B1 , then f1 = α1 Vα + b1 Vβ + c1 Rule 2: If Vα is A2 and Vβ is B2 , then f2 = α2 Vα + b2 Vβ + c2 .. . Rule n: If Vα is An and Vβ is Bn , then fn = αn Vα + bn Vβ + cn . Here, An and Bn represent fuzzy membership functions (MFs) for inputs Vα , Vβ . an , bn , cn show attributes of fuzzy rule base. ANFIS has five layers such as fuzzy layer, product layer, normalized layer, defuzzification layer and total output layer. The ANFIS inputs are Vα and Vβ that interfere for each node from An and Bn . An and Bn are the linguistic functions utilized to define MFs. ANFIS utilize fuzzy 1850132-4


triangular membership function. It is evaluated as follows:  x−z   c − z z ≤ x < c, f (x; z, c, m) = (1)   x − mc ≤ x < m . c−m Here, z and m represent bound to the width of MFs. c shows centre of MFs.30


2.2. Random forest The random forest contains several trees and selects random subsets of a number of different predictors tested at each node.31 To grow the trees, a deterministic technique is used to select each tree from a random set of attributes. The node is utilized to decompose the nodes for decreasing the total number through description available for analysis. The sampled random vector and standard random forest are mixed as an estimator for each tree by using similar distribution for all trees. It contains input vector X = x1 , x2 , . . . , xp , where a p-dimensional input vector works in building a forest. Inside the forest a set of K trees T1 (x), T2 (x), . . . , Tk (x), the output of each tree estimates the actual value a ˆY1 = T1 (X), . . . , a ˆYm = Tm (X), where m = 1, . . . , K. The final result of it is the mean of all the values predicted by different trees. EstimateRF (X) =

k 1X yˆk (X) . k

(2)

k=1

The training dataset is independently taken from the input and output D = D1 , D2 , . . . , Dn = (x1 , y1 ), (x2 , y2 ), . . . , (xn , yn ), where xi , i = 1, . . . , n, is the training dataset for input vector and yi , i = 1, . . . , n, is training dataset for output vector. Each tree is grown using the training approach. The estimated error and accuracy is evaluated for the random forest using the minimization of mean square error (MSE). Determining the optimum trees in forest focuses on MSE. Testing data is communicated using each split node, by sending it either to right or to left child until ending up at a leaf node. m 1 X ˆ (Y (Xi ) − Yi )2 . (3) MSE ≈ MSEOOB = m i=1 Here, Yˆ (Xi ) represents estimated output of trees in forest corresponding to a given input sample, Yi shows observed output and m shows a total number of samples. However, a random forest has difficulties in selecting the number of trees. In this work, we have taken a number of trees = 100.31 2.3. Designed ensembling-based model This step describes the detail of the proposed ensembling-based machine learning model for drug synergy prediction. Figure 2 demonstrates the different stages of the 1850132-5



Fig. 2.

Flowchart of the proposed technique.

proposed ensembling-based machine learning technique. Every stage shows a building block of the proposed model. The following section provides brief description of every stage. Stage 1: Initially, actually drug synergy score dataset is loaded into the R-tool’s memory. This data contains multiple attributes along with the target class, i.e. drug synergy score. Then, this dataset is decomposed into training and testing data. Then, the target class ‘drug synergy score’ will remove it for validation purpose. Stage 2: In this stage, well-known machine learning techniques such as Random forest, GFS.GCCL, ANFIS and DENFIS are implemented on the drug synergy score training dataset to form their respective models. Stage 3: In this stage, trained machine learning models are implemented on the testing data to predict drug synergy score. 1850132-6


Stage 4: This stage is an ensembling stage. Drug score predicted by Random forest, GFS.GCCL, ANFIS and DENFIS models are integrated into this stage. The integration is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The biased weighted aggregation is computed as follows: Prediction = 0.35 ∗ GFS.GCCL.pred + 0.25 ∗ DENFIS.pred + 0.20 ∗ ANFIS.pred + 0.20 ∗ RF.pred.

(4)


Here, pred represents predicted values by given machine learning models. 3. Performance Analysis This section provides the comparative analysis of proposed and existing machine learning techniques. The proposed and other methods have been implemented on Intel core i7 processor with 16 GB RAM. The simulation environment is designed in well-known R-tool. Subsequent sections describe experimental results of proposed and other techniques when they are applied on drug synergy score data.32 The synergy score data which has been used in this research work for validation purpose contains two terms i.e. (a) Maximum concentration (uM) of drug A and (b) Maximum concentration (uM) of drug B. Therefore, the significance of synergy score data depends upon the different concentrations of different drugs. Therefore, the proposed machine learning model has been implemented on the training data set containing the different concentration of different drugs to predict the synergy score.32 It has been evaluated using coefficient of drug interaction (CDI) as follows: CDI =

AB . A×B

(5)

Here, AB is the ratio of the 2-drug combination group to the control group. A or B is the ratio of the single drug group to the control group. The CDI < 1 indicates synergism, especially CDI < 0.7 indicates a significantly synergistic effect, CDI = 1 indicates additivity and CDI > 1 indicates antagonism. In this research work, 10-fold cross-validation has been used to test the proposed ensemble-based machine learning model during the training phase to evaluate overfitting issues. To achieve 10-fold cross-validation, initially training data has been divided into 10 equal subsets (fold). Keep the 1-fold as validation set and keep other 9-fold in the cross-validation training set. Train proposed model using the cross-validation training set and evaluate the accuracy of the proposed model by validating the predicted values against the validation set. Similarly, accuracy of all 10-fold have been evaluated. To overcome the issue of overfitting, mean of calculated accuracies have been evaluated. Each fold has been utilized for validation just once. Therefore, 10-fold cross-validation guarantees that the proposed trained model does not suffer from the overfitting issue. 1850132-7


3.1. Accuracy analysis


Accuracy is a performance metric of a machine learning model that reflects the proportionate number of times that the developed model is correct when implemented to data. In regression-based machine learning models, accuracy depends upon a factor so-called acceptance error (Ae ). It allows us to accept the predicted data as accurate with some amount of error. Ae converts the difference between actual and predicted data to the binary form. In which some 1s determine the accuracy of data. In the same way, number of 0s determine the error rate. The accuracy can be computed as follows: PN Accuracy =

i=1

di

N

× 100 .

(6)

Here, N represents total number of records in the predicted data. Also, a binary difference (di ) can be computed as follows:

di =

( 1 if |ai − pi | ≤ Ae ,

(7)

0 otherwise.

Here, ai and pi represent actual and predicted drug energy scores, respectively. Thus, accuracy ranges from 0 to 100. The technique with accuracy approaching to 100 is more efficient compared with other techniques. In our experiment, we have taken Ae = 2. Table 1 depicts the accuracy analysis of the proposed ensemble-based machine learning technique. It has been observed that the proposed technique outperforms others because it has better accuracy rate as compared to other machine learning techniques. In Table 1, data is trained and tested on same drug synergy score data by dividing it into training and testing data. It has been observed that the proposed technique outperforms other machine learning models because the proposed technique has shown better accuracy as compared to different techniques. Table 1. Dataset Linear model J48 SVM Neural network Random forest CART ANFIS DENFIS GFS.GCCL Proposed

40% 86.41 88.90 90.94 86.49 91.48 87.33 91.04 92.90 92.36 93.11

± ± ± ± ± ± ± ± ± ±

Comparative analysis of accuracy. 50%

5.9 5.2 3.8 4.3 3.1 3.9 3.2 2.9 2.6 2.3

88.78 87.24 91.40 86.09 90.13 90.94 88.38 93.95 93.57 94.05

± ± ± ± ± ± ± ± ± ±

60% 5.1 4.8 3.6 6.3 3.7 3.7 3.6 2.8 2.7 2.6

89.23 85.82 91.01 85.04 91.16 89.64 90.35 93.73 94.64 95.45

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± ± ± ± ± ± ± ± ± ±

70% 4.7 4.7 2.9 5.6 3.6 3.3 3.1 2.7 2.7 2.5

91.54 87.74 89.18 86.68 89.18 90.36 90.29 93.62 94.78 96.08

± ± ± ± ± ± ± ± ± ±

80% 3.8 4.3 3.7 5.8 3.1 3.6 3.2 2.8 2.3 2.1

91.92 88.48 90.43 89.44 89.30 89.50 90.77 94.51 95.81 96.79

± ± ± ± ± ± ± ± ± ±

3.6 3.7 2.7 4.5 2.8 3.2 2.7 2.3 2.1 2.1

Prediction of drug synergy in cancer using ensemble-based machine learning techniques Table 2. Dataset


Linear model J48 SVM Neural network Random forest CART ANFIS DENFIS GFS.GCCL Proposed

Comparative analysis of root mean squared error.

40% 15.12 14.29 13.61 15.09 13.43 14.81 13.57 12.96 13.13 12.89

± ± ± ± ± ± ± ± ± ±

50% 2.9 3.2 2.8 2.4 2.3 2.6 2.1 1.6 1.4 1.3

14.33 14.84 13.46 15.22 13.88 13.61 15.46 12.60 12.73 12.57

± ± ± ± ± ± ± ± ± ±

60% 2.7 3.2 2.9 2.6 2.6 2.4 1.9 1.8 1.7 1.5

14.18 15.32 13.58 15.58 13.53 14.04 13.81 12.68 12.37 12.11

± ± ± ± ± ± ± ± ± ±

70% 2.8 2.9 3.4 2.9 2.8 2.6 1.7 1.4 1.5 1.3

13.41 14.67 14.19 15.03 14.20 13.80 13.82 12.71 12.33 11.91

± ± ± ± ± ± ± ± ± ±

80% 2.9 2.8 3.1 2.4 1.9 2.4 1.8 1.7 1.5 1.2

13.28 14.43 13.78 14.11 14.15 14.09 13.66 12.42 11.98 11.66

± ± ± ± ± ± ± ± ± ±

3.1 2.6 2.8 2.7 2.2 2.3 1.4 1.3 1.2 1.3

3.2. Root mean square error analysis Root mean square error (RMSE) is a measure of the difference between actual and predicted values. RMSE denotes standard deviation of the error between predicted and actual values so-called residuals or prediction errors. RMSE of predicted values yˆt for attributes t of a regression’s dependent attribute yt is evaluated for n different predictions as follows: r Pn yt − yt )2 t=1 (ˆ RMSD = . (8) n Table 2 shows the comparison of RMSE analysis between the proposed and other machine learning techniques. It depicts that the proposed technique outperforms others because it has lesser RMSE as compared to others. In Table 2, data is trained and tested on same drug synergy score data by dividing it into training and testing data. It has been observed that if we increase the size of training data, i.e. from 40% to 80%, even then proposed technique outperforms others. Therefore, proposed technique has more realistic results as compared to other machine learning techniques. 3.3. Coefficient of correlation analysis The coefficient of correlation (r) so-called Pearson correlation coefficient measures degree of linear correlation between two attributes A and B. r ∈ [−1, 1], where 1 is represented positive linear correlation, 0 indicates no linear correlation and −1 represents negative linear correlation. Mathematically, r can be computed as follows: cv (A, B) . (9) r= σA σB Here, cv represents covariance between A and B. σA shows standard deviation of A and σB shows standard deviation of B. cv can be computed as follows: cv (A, B) = [(A − µA )(B − µB )] .

(10)

Here, µA and µB represent mean of A and y, respectively. shows expectation. 1850132-9

H. Singh, P. S. Rana & U. Singh Table 3. Dataset


Linear model J48 SVM Neural network Random forest CART ANFIS DENFIS GFS.GCCL Proposed

Comparative analysis of coefficient of correlation.

40% 0.87 0.90 0.92 0.87 0.92 0.88 0.92 0.94 0.93 0.94

± ± ± ± ± ± ± ± ± ±

50%

0.09 0.07 0.05 0.11 0.06 0.08 0.05 0.04 0.04 0.04

0.90 0.88 0.92 0.87 0.91 0.92 0.86 0.94 0.95 0.95

± ± ± ± ± ± ± ± ± ±

0.07 0.09 0.06 0.10 0.07 0.06 0.11 0.04 0.04 0.03

60% 0.91 0.87 0.92 0.86 0.92 0.91 0.91 0.94 0.96 0.96

± ± ± ± ± ± ± ± ± ±

0.07 0.08 0.05 0.13 0.05 0.05 0.07 0.04 0.03 0.03

70% 0.92 0.89 0.90 0.88 0.90 0.91 0.91 0.93 0.96 0.97

± ± ± ± ± ± ± ± ± ±

0.05 0.07 0.07 0.09 0.07 0.06 0.06 0.05 0.03 0.02

80% 0.93 0.89 0.91 0.90 0.90 0.90 0.92 0.94 0.97 0.98

± ± ± ± ± ± ± ± ± ±

0.04 0.08 0.05 0.08 0.05 0.07 0.05 0.04 0.02 0.01

Now, r can be rewritten as follows: r=

[(A − µA )(B − µB )] . σA σB

(11)

Therefore, formula for r can be represented in terms of uncentered moments. Since, µA = [A],

(12)

µB = ,

(13)

2 σA = [(A − [A])2 ] = [A2 ] − [[A]]2 ,

(14)

2 σB = [(B − [B])2 ] = [B 2 ] − [[B]]2 ,

(15)

[(A − µA )(B − µB )] = [(A − [A])(B − [B])] = [AB] − [A][B].

(16)

Therefore, r can also be rewritten as follows: r= p

[A2 ]

[AB] − [A][B] p . − [[A]]2 × [B 2 ] − [[B]]2

(17)

Table 3 demonstrates the coefficient of correlation analysis between the proposed ensemble-based machine learning technique with other techniques. It has been observed that the proposed technique has significant correlation coefficient than other machine learning techniques. It has also been observed that the proposed technique has better, as well as more consistent results as compared to other techniques, as proposed techniques show minor variation than others. 4. Receiver Operating Characteristic Curve A receiver operating characteristic curve (ROC curve) is a graphical plot that shows the diagnostic ability of a binary classifier model as its discrimination threshold is varied. Since, our data is regression-based, we have used threshold value (t1 = 0.5) to convert synergy score into classification data. The ROC curve is developed by plotting the true positive rate (sensitivity) against the false positive 1850132-10



Fig. 3.

(Color online) ROC curve.

rate (1 specificity) at various threshold settings. Figure 3 demonstrates that the proposed model has significantly more sensitivity than existing models. 5. Conclusion Drug synergy is commonly utilized in treating the most terrible diseases, such as cancer. The primary significance of drug synergy score prediction is to realize synergistic therapeutic effect, dose and toxicity reduction, and to decrease the induction of drug resistance. Current advancements in cancer have discovered that the disease cannot be understood simply through the examination of genetic mutations within cancer cells. Therefore, examination of drug synergy score needs well-organized regression models to decrease the prediction errors. In this paper, a neuro-fuzzy-based ensembling approach has been designed. Ensembling has been achieved by evaluating the biased weighted aggregation of predicted data by selected models, i.e. Random forest, GFS.GCCL, ANFIS and DENFIS-based machine learning models. The proposed technique has been evaluated on drug synergy score data and also compared with nine well-known existing machine learning models. It has been evaluated that mean improvement of proposed technique over other machine learning models in terms of accuracy, root mean square error and correlation coefficient are 4.7894%, 2.7467% and 3.1327%, respectively. Therefore, proposed technique is more efficient for designing a real-time drug synergy estimator.

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