The Discovery of New Drug-Target Interactions for Breast Cancer Treatment
Abstract
:1. Introduction
2. Results
2.1. Performance Evaluation
2.2. Features Generation
2.2.1. Parameter Setting for PsePSSM and DCCA Coefficient
2.2.2. The Dimensionality of the Generated Features
2.3. Predictive Performance of Lasso for Dimensionality Reduction
2.4. Predictive Performance of SMOTE for Imbalanced Datasets
2.5. Predictive Performance of RF for DTIs Prediction
2.6. Predictive Performance of PsePDC-DTIs Compared with State-of-the-Art Methods
2.7. Predictive Performance of PsePDC-DTIs Compared with State-of-the-Art Methods
3. Discussion
4. Materials and Methods
4.1. Datasets
4.1.1. Benchmark Datasets
4.1.2. DTIs Dataset Constructed by Drugs of FDA-Approved and Targets of Breast Cancer
4.2. Methods for Features Generation
4.2.1. Pseudo-Position Specific Scoring Matrix (PsePSSM)
4.2.2. Detrended Cross-Correlation Analysis Coefficient (DCCA Coefficient)
4.2.3. FP2 Molecular Fingerprint
4.3. Lasso for Dimensionality Reduction of Features
4.4. SMOTE for High-Dimensional Class-Imbalanced Data
4.5. RF for DTIs Prediction
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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Models | NR | GPCR | IC | E |
---|---|---|---|---|
NetCBP [26] | 0.8394 | 0.8235 | 0.8034 | 0.8251 |
Huang et al. [27] | 0.9634 | 0.9053 | 0.9382 | 0.9601 |
Bigram-PSSM [21] | 0.8690 | 0.8720 | 0.8890 | 0.9480 |
iDTI-ESBoost [20] | 0.9285 | 0.9322 | 0.9369 | 0.9689 |
Li et al. [28] | 0.9300 | 0.9171 | 0.8856 | 0.9288 |
KBMF2K [29] | 0.8240 | 0.8570 | 0.7990 | 0.8320 |
NRLMF [30] | 0.9500 | 0.9690 | 0.9890 | 0.9870 |
PsePDC-DTIs | 0.9886 | 0.9923 | 0.9956 | 0.9983 |
Models | NR | GPCR | IC | E |
---|---|---|---|---|
Bigram-PSSM [21] | 0.4110 | 0.2820 | 0.3900 | 0.5460 |
iDTI-ESBoost [20] | 0.7900 | 0.5000 | 0.4800 | 0.6800 |
NRLMF [30] | 0.7280 | 0.7490 | 0.9060 | 0.8920 |
PsePDC-DTIs | 0.9875 | 0.9923 | 0.9958 | 0.9984 |
Drug | Drug_Name | Target | Target_Name | Prob |
---|---|---|---|---|
DB00201 | Caffeine | hsa3783 | KCNN4 | 0.988 |
DB00277 | Theophylline | hsa3783 | KCNN4 | 0.982 |
DB01412 | Theobromine | hsa3783 | KCNN4 | 0.93 |
DB00530 | Erlotinib | hsa238 | ALK | 0.886 |
DB00806 | Pentoxifylline | hsa3783 | KCNN4 | 0.884 |
DB00824 | Enprofylline | hsa3783 | KCNN4 | 0.866 |
DB00530 | Erlotinib | hsa2263 | FGFR2 | 0.864 |
DB00661 | Verapamil | hsa57719 | ANO8 | 0.846 |
DB01303 | Oxtriphylline | hsa3783 | KCNN4 | 0.844 |
DB08916 | Afatinib | hsa2263 | FGFR2 | 0.806 |
Datasets | Drugs | Targets | Interactions | Positive Samples | Negative Samples | Sample Ratio |
---|---|---|---|---|---|---|
Enzyme | 445 | 664 | 2926 | 2926 | 292,554 | 99.98 |
IC | 210 | 204 | 1476 | 1476 | 41,364 | 28.02 |
GPCR | 223 | 95 | 635 | 635 | 20,550 | 32.36 |
NR | 54 | 26 | 90 | 90 | 1314 | 14.60 |
Total | 932 | 989 | 5127 | 5127 | 355,782 | - |
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Song, J.; Xu, Z.; Cao, L.; Wang, M.; Hou, Y.; Li, K. The Discovery of New Drug-Target Interactions for Breast Cancer Treatment. Molecules 2021, 26, 7474. https://doi.org/10.3390/molecules26247474
Song J, Xu Z, Cao L, Wang M, Hou Y, Li K. The Discovery of New Drug-Target Interactions for Breast Cancer Treatment. Molecules. 2021; 26(24):7474. https://doi.org/10.3390/molecules26247474
Chicago/Turabian StyleSong, Jiali, Zhenyi Xu, Lei Cao, Meng Wang, Yan Hou, and Kang Li. 2021. "The Discovery of New Drug-Target Interactions for Breast Cancer Treatment" Molecules 26, no. 24: 7474. https://doi.org/10.3390/molecules26247474