Background: Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.Methods: A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validate the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo.Results: The experimental results show that the DLDTI model achieves promising performance under 5-fold cross-validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models.Conclusions: DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.
Objective. To analyze and evaluate the effectiveness of traditional Chinese medicine decoction (TCMD) in the treatment of cardiac neurosis (CN) complicated with depression and anxiety. Methods. Relevant literature on TCMD in the treatment of CN complicated with depression and anxiety was retrieved from Chinese and English databases, and the retrieved literature was included and excluded. The quality of the retrieved literature was evaluated according to the Cochrane system, and the meta-analysis was undertaken with RevMan5.3 software. Results. A total of 10 papers were included, including 686 patients and 349 patients in the treatment group (131 males and 218 females), and there were 337 cases in the control group (131 males and 206 females). The results of the meta-analysis showed that compared with the control group, TCMD could significantly improve palpitation (MD = 0.78, 95% CI = 0.55–1.01, P < 0.00001 ), chest tightness/chest pain (MD = 0.53, 95% CI = 0.10–0.97, P < 0.05 ), shortness of breath (MD = 0.60, 95% CI = 0.26–0.93, P = 0.0005 ), and TCM syndrome score (MD = 5.91, 95% CI = 4.32–7.49, P < 0.00001 ), reduce resting heart rate (MD = 13.21, 95% CI = 9.01–17.40, P < 0.00001 ), and could reduce psychological scale scores, such as Zung’s self-rating Depression Scale (MD = 7.90, 95% CI = 4.98–10.83, P < 0.00001 ), Zung’s self-rating Anxiety Scale (MD = 6.55, 95% CI = 4.25–8.86, P < 0.00001 ), Hamilton Depression Scale (MD = 4.46, 95% CI = 3.00–5.92, P < 0.00001 ), and Hamilton Anxiety Scale (MD = 3.35, 95% CI = 1.85–4.85, P < 0.0001 ). The differences were statistically significant. Tonic drugs were commonly used; Angelica sinensis, Poria cocos, Polygala tenuifolia, Wild jujube kernel, and Bupleurum were the most frequently used traditional Chinese medicines. Conclusion. TCMD mainly composed of tonic and tranquilizing herbs could significantly improve the clinical symptoms of patients with CN and reduce heart rate, anxiety, and depression scores. However, the evidence grade of the clinical research was limited by the quality of the included literature, and more high-quality clinical trials are still needed for further verification. This trial was registered with PROSPERO: CRD42022312164
Background: Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.Methods: A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validate the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo.Results: The experimental results show that the DLDTI model achieves promising performance under 5-fold cross-validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models.Conclusions: DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.
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