Prediction of Drug–Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method
Abstract
:1. Introduction
2. Results
2.1. Evaluation Metrics
2.2. Parameter Discussion
2.3. Performance Evaluations on Four Golden Standard Datasets
2.4. Comparison Results between LPQ-Based Model and the Proposed Method
2.5. Comparison with SVM and KNN Classifier
2.6. Comparison with Different Methods on the Same Dataset
2.7. Performance on the Independent Dataset
3. Materials and Methods
3.1. Data Collection
3.2. Characterization of Drug Molecules
3.3. Representation of Target Proteins
3.4. Feature Extraction Method
3.5. Rotation Forest Classifier (RoF)
- (1)
- Choose a suitable parameter M for which U can be randomly split into M disjointed subsets, with the number of features contained in the feature subset being equal to L/M.
- (2)
- Let represent the jth feature subset and use it to train the classifier . The sample subset is constructed by a non-empty subset, which is randomly selected from a certain proportion.
- (3)
- Apply PCA [52] on to order the coefficients stored in matrix .
- (4)
- The coefficients obtained from the matrix are used to construct a sparse rotation matrix , which can be defined as follows:
4. Case Study
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Sample Availability
References
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Test Set | ACC. (%) | PR. (%) | Sen. (%) | MCC (%) | AUC |
---|---|---|---|---|---|
1 | 89.40 | 89.80 | 87.88 | 81.00 | 0.9452 |
2 | 88.72 | 89.02 | 88.72 | 79.98 | 0.9516 |
3 | 88.80 | 90.84 | 85.96 | 80.07 | 0.9499 |
4 | 88.80 | 91.68 | 85.22 | 80.06 | 0.9435 |
5 | 90.34 | 92.49 | 88.71 | 82.53 | 0.9585 |
Average | 89.21 ± 0.69 | 90.77 ± 1.40 | 87.30 ± 1.62 | 80.73 ± 1.09 | 0.9498 ± 0.0059 |
Test Set | ACC. (%) | PR. (%) | Sen. (%) | MCC (%) | AUC |
---|---|---|---|---|---|
1 | 85.08 | 85.08 | 85.08 | 74.62 | 0.9324 |
2 | 84.41 | 86.26 | 80.14 | 73.49 | 0.9154 |
3 | 84.92 | 87.06 | 84.59 | 74.27 | 0.9233 |
4 | 85.59 | 83.59 | 89.40 | 75.22 | 0.9200 |
5 | 87.46 | 85.92 | 87.77 | 78.01 | 0.9440 |
Average | 85.49 ± 1.18 | 85.58 ± 1.32 | 85.40 ± 3.54 | 75.12 ± 1.73 | 0.9270 ± 0.0113 |
Test Set | ACC. (%) | PR. (%) | Sen. (%) | MCC (%) | AUC |
---|---|---|---|---|---|
1 | 79.13 | 81.06 | 79.26 | 66.88 | 0.8581 |
2 | 76.77 | 77.60 | 75.78 | 64.33 | 0.8388 |
3 | 79.53 | 80.16 | 78.91 | 67.44 | 0.8734 |
4 | 83.46 | 84.35 | 80.17 | 72.24 | 0.8914 |
5 | 86.22 | 84.38 | 87.80 | 76.23 | 0.9258 |
Average | 81.02 ± 3.77 | 81.51 ± 2.90 | 80.38 ± 4.47 | 69.42 ± 4.76 | 0.8775 ± 0.0332 |
Test Set | ACC. (%) | PR. (%) | Sen. (%) | MCC (%) | AUC |
---|---|---|---|---|---|
1 | 72.22 | 60.00 | 59.23 | 57.67 | 0.7642 |
2 | 77.78 | 72.73 | 88.89 | 64.58 | 0.7963 |
3 | 80.56 | 73.69 | 87.50 | 68.43 | 0.8109 |
4 | 66.67 | 78.95 | 65.22 | 53.74 | 0.7609 |
5 | 75.00 | 76.19 | 80.00 | 61.73 | 0.7453 |
Average | 74.44 ± 5.34 | 72.31 ± 7.29 | 78.17 ± 10.64 | 61.23 ± 5.75 | 0.7755 ± 0.0271 |
Negative Samples | ACC. (%) | PR. (%) | Sen. (%) | MCC (%) | AUC |
---|---|---|---|---|---|
Sample 1 | 81.73 ± 2.18 | 82.47 ± 2.48 | 80.65 ± 3.93 | 70.03 ± 2.94 | 0.8882 ± 0.0126 |
Sample 2 | 81.13 ± 1.80 | 83.04 ± 2.49 | 80.59 ± 4.64 | 70.51 ± 2.36 | 0.8904 ± 0.0167 |
Sample 3 | 81.97 ± 1.06 | 82.19 ± 1.79 | 81.59 ± 3.39 | 70.29 ± 1.48 | 0.8876 ± 0.0046 |
Sample 4 | 81.10 ± 1.18 | 82.04 ± 1.61 | 79.53 ± 4.17 | 69.18 ± 1.65 | 0.8881 ± 0.0162 |
Sample 5 | 81.57 ± 1.53 | 82.79 ± 2.66 | 81.23 ± 4.59 | 69.76 ± 2.06 | 0.8900 ± 0.0124 |
Dataset | Descriptor | ACC. (%) | PR. (%) | Sen. (%) | MCC (%) | AUC |
---|---|---|---|---|---|---|
Enzyme | LPQ | 87.45 ± 3.87 | 88.62 ± 5.32 | 86.20 ± 2.02 | 78.24 ± 5.49 | 0.9329 ± 0.0358 |
DTCWT | 89.21 ± 0.69 | 90.77 ± 1.40 | 87.30 ± 1.62 | 80.73 ± 1.09 | 0.9498 ± 0.0059 | |
Ion Channel | LPQ | 84.07 ± 3.59 | 83.10 ± 3.98 | 85.49 ± 4.85 | 73.43 ± 5.03 | 0.9008 ± 0.0404 |
DTCWT | 85.49 ± 1.18 | 85.58 ± 1.32 | 85.40 ± 3.54 | 75.12 ± 1.73 | 0.9270 ± 0.0113 | |
GPCRs | LPQ | 79.61 ± 4.26 | 78.69 ± 6.04 | 81.07 ± 3.76 | 67.71 ± 4.69 | 0.8474 ± 0.0402 |
DTCWT | 81.02 ± 3.77 | 81.51 ± 2.90 | 80.38 ± 4.47 | 69.42 ± 4.76 | 0.8775 ± 0.0332 | |
NRs | LPQ | 71.11 ± 3.17 | 70.03 ± 10.36 | 75.55 ± 13.97 | 56.53 ± 3.54 | 0.7403 ± 0.0667 |
DTCWT | 74.44 ± 5.34 | 72.31 ± 7.29 | 78.17 ± 10.64 | 61.23 ± 5.75 | 0.7755 ± 0.0271 |
Dataset | Model | ACC. (%) | PR. (%) | Sen. (%) | MCC (%) | AUC |
---|---|---|---|---|---|---|
Enzyme | SVM | 77.25 ± 0.73 | 79.84 ± 1.31 | 72.86 ± 2.45 | 64.67 ± 0.89 | 0.8012 ± 0.0191 |
KNN | 80.53 ± 1.85 | 77.42 ± 2.20 | 86.06 ± 3.17 | 68.46 ± 2.17 | 0.8050 ± 0.0168 | |
RoF | 89.21 ± 0.69 | 90.77 ± 1.40 | 87.30 ± 1.62 | 80.73 ± 1.09 | 0.9498 ± 0.0059 | |
Ion Channel | SVM | 71.08 ± 1.93 | 70.28 ± 1.56 | 73.21 ± 4.58 | 58.77 ± 1.61 | 0.7683 ± 0.0224 |
KNN | 79.02 ± 2.28 | 75.11 ± 3.60 | 86.72 ± 3.89 | 66.41 ± 2.68 | 0.7901 ± 0.0204 | |
RoF | 85.49 ± 1.18 | 85.58 ± 1.32 | 85.40 ± 3.54 | 75.12 ± 1.73 | 0.9270 ± 0.0113 | |
GPCRs | SVM | 61.65 ± 2.44 | 62.26 ± 4.33 | 59.53 ± 1.50 | 52.72 ± 1.09 | 0.6652 ± 0.0252 |
KNN | 63.86 ± 3.83 | 61.47 ± 3.05 | 74.65 ± 2.21 | 52.86 ± 2.61 | 0.6379 ± 0.0408 | |
RoF | 81.02 ± 3.77 | 81.51 ± 2.90 | 80.38 ± 4.47 | 69.42 ± 4.76 | 0.8775 ± 0.0332 | |
NRs | SVM | 62.22 ± 5.41 | 61.99 ± 11.70 | 60.90 ± 9.47 | 52.22 ± 3.51 | 0.6220 ± 0.0611 |
KNN | 48.33 ± 6.97 | 48.65 ± 9.71 | 46.68 ± 2.26 | 49.58 ± 0.77 | 0.4775 ± 0.0728 | |
RoF | 74.44 ± 5.34 | 72.31 ± 7.29 | 78.17 ± 10.64 | 61.23 ± 5.75 | 0.7755 ± 0.0271 |
Method | Enzyme | GPCRs | Ion Channel | NRs |
---|---|---|---|---|
Yamanishi et al. | 0.845 | 0.812 | 0.731 | 0.830 |
KBMF2K | 0.832 | 0.857 | 0.799 | 0.824 |
MLCLE | 0.842 | 0.850 | 0.795 | 0.790 |
AM-PSSM | 0.843 | 0.839 | 0.722 | 0.767 |
SIMCOMP | 0.863 | 0.867 | 0.776 | 0.856 |
DBSI | 0.8075 | 0.8022 | 0.8029 | 0.7578 |
NETCBP | 0.8251 | 0.8235 | 0.8034 | 0.8394 |
Our Method | 0.9498 | 0.8775 | 0.9270 | 0.7755 |
Dataset | Drug | Target Protein | Interactions |
---|---|---|---|
Enzyme | 445 | 664 | 2926 |
Ion Channel | 210 | 204 | 1476 |
NRs | 54 | 26 | 90 |
GPCRs | 223 | 95 | 635 |
Drugbank-approved | 1555 | 1591 | 5831 |
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Pan, J.; Li, L.-P.; You, Z.-H.; Yu, C.-Q.; Ren, Z.-H.; Chen, Y. Prediction of Drug–Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method. Molecules 2021, 26, 5359. https://doi.org/10.3390/molecules26175359
Pan J, Li L-P, You Z-H, Yu C-Q, Ren Z-H, Chen Y. Prediction of Drug–Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method. Molecules. 2021; 26(17):5359. https://doi.org/10.3390/molecules26175359
Chicago/Turabian StylePan, Jie, Li-Ping Li, Zhu-Hong You, Chang-Qing Yu, Zhong-Hao Ren, and Yao Chen. 2021. "Prediction of Drug–Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method" Molecules 26, no. 17: 5359. https://doi.org/10.3390/molecules26175359