DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning
Abstract
:1. Introduction
2. Results
2.1. Model Determination Using Full-Scale Stabilities in Predicting Regulatory Effects
2.2. Performance of SM-Mediated Regulatory Effects on miRNA Expression Using an Independent Dataset
2.3. Regulatory Effect Prediction on Guilt-by-Association SM-miR Relations
2.4. Regulatory Effect Prediction Using Recurrent miRNAs or Small-molecule compounds
2.5. Regulatory Effect Prediction Using Novel miRNAs or Small-Molecule Compounds
2.6. Reference Map of the Relations Based on Gene-Expression-Perturbed miRNAs or Small Molecules
2.7. Prediction of Regulatory Effects for Indirect SM-miR Relations
2.8. Inference of SM-Disease Associations for Drug Discovery and Repositioning
3. Discussion
4. Materials and Methods
4.1. Experimentally Verified SM-miR Relations
4.2. SM-miR Relations Based on Similarity Inference
4.3. Dataset
4.4. Definition of the Small-Molecule-Mediated Regulatory Effects on miRNA Expression
4.5. Feature Representation
4.6. Deep Learning Methodology
4.7. Training Deep Learning Algorithms
4.8. Overfitting Prevention
4.9. Ensemble of Deep Learning Models
4.10. Performance Assessment
4.11. Network Inference Approach for Novel SM Drugs and miRNA Targets Based on Similarity
4.12. Psmir Relations Based on miRNA-Perturbed Gene Expression Profiles
4.13. VerSe with Pharmacogenomic miRNAs
4.14. miRNA-Cancer Database
4.15. Connectivity Scoring of SM-Cancer Associations
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
References
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Method | AUC | AUCPR | ACC | bACC | Precision | Recall | MCC | F1score | Jaccard |
---|---|---|---|---|---|---|---|---|---|
AlexNet | 0.779 | 0.827 | 0.691 | 0.688 | 0.730 | 0.712 | 0.374 | 0.721 | 0.563 |
BiRNN | 0.790 | 0.828 | 0.691 | 0.681 | 0.708 | 0.763 | 0.367 | 0.735 | 0.580 |
RNN | 0.782 | 0.817 | 0.712 | 0.704 | 0.732 | 0.769 | 0.412 | 0.750 | 0.600 |
Seq2Seq | 0.796 | 0.824 | 0.698 | 0.687 | 0.712 | 0.776 | 0.381 | 0.742 | 0.590 |
CNN | 0.796 | 0.829 | 0.723 | 0.710 | 0.726 | 0.814 | 0.432 | 0.767 | 0.623 |
ConvMixer64 | 0.804 | 0.819 | 0.691 | 0.711 | 0.850 | 0.545 | 0.436 | 0.664 | 0.497 |
DSConv | 0.791 | 0.828 | 0.687 | 0.663 | 0.673 | 0.859 | 0.359 | 0.755 | 0.606 |
LSTMCNN | 0.822 | 0.848 | 0.759 | 0.749 | 0.760 | 0.833 | 0.507 | 0.795 | 0.660 |
MobileNetV2 | 0.781 | 0.810 | 0.723 | 0.726 | 0.780 | 0.705 | 0.448 | 0.741 | 0.588 |
ResNet18 | 0.789 | 0.838 | 0.701 | 0.704 | 0.759 | 0.686 | 0.404 | 0.721 | 0.563 |
ResNet50 | 0.804 | 0.820 | 0.730 | 0.726 | 0.758 | 0.763 | 0.452 | 0.760 | 0.613 |
SCAResNet18 | 0.792 | 0.821 | 0.723 | 0.713 | 0.734 | 0.795 | 0.433 | 0.763 | 0.617 |
DeepsmirUD-all | 0.840 | 0.866 | 0.763 | 0.756 | 0.778 | 0.808 | 0.516 | 0.792 | 0.656 |
DeepsmirUD-top | 0.843 | 0.866 | 0.781 | 0.779 | 0.810 | 0.795 | 0.556 | 0.803 | 0.670 |
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Sun, J.; Ru, J.; Ramos-Mucci, L.; Qi, F.; Chen, Z.; Chen, S.; Cribbs, A.P.; Deng, L.; Wang, X. DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning. Int. J. Mol. Sci. 2023, 24, 1878. https://doi.org/10.3390/ijms24031878
Sun J, Ru J, Ramos-Mucci L, Qi F, Chen Z, Chen S, Cribbs AP, Deng L, Wang X. DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning. International Journal of Molecular Sciences. 2023; 24(3):1878. https://doi.org/10.3390/ijms24031878
Chicago/Turabian StyleSun, Jianfeng, Jinlong Ru, Lorenzo Ramos-Mucci, Fei Qi, Zihao Chen, Suyuan Chen, Adam P. Cribbs, Li Deng, and Xia Wang. 2023. "DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning" International Journal of Molecular Sciences 24, no. 3: 1878. https://doi.org/10.3390/ijms24031878