1
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Qu J, Song Z, Cheng X, Jiang Z, Zhou J. Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction. PeerJ 2023; 11:e15889. [PMID: 37641598 PMCID: PMC10460564 DOI: 10.7717/peerj.15889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/21/2023] [Indexed: 08/31/2023] Open
Abstract
Background A growing number of experiments have shown that microRNAs (miRNAs) can be used as target of small molecules (SMs) to regulate gene expression for treating diseases. Therefore, identifying SM-related miRNAs is helpful for the treatment of diseases in the domain of medical investigation. Methods This article presents a new computational model, called NIRBMSMMA (neighborhood-based inference (NI) and restricted Boltzmann machine (RBM)), which we developed to identify potential small molecule-miRNA associations (NIRBMSMMA). First, grounded on known SM-miRNAs associations, SM similarity and miRNA similarity, NI was used to predict score of an unknown SM-miRNA pair by reckoning the sum of known associations between neighbors of the SM (miRNA) and the miRNA (SM). Second, utilizing a two-layered generative stochastic artificial neural network, RBM was used to predict SM-miRNA association by learning potential probability distribution from known SM-miRNA associations. At last, an ensemble learning model was conducted to combine NI and RBM for identifying potential SM-miRNA associations. Results Furthermore, we conducted global leave one out cross validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation to assess performance of NIRBMSMMA based on three datasets. Results showed that NIRBMSMMA obtained areas under the curve (AUC) of 0.9912, 0.9875, 0.8376 and 0.9898 ± 0.0009 under global LOOCV, miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation based on dataset 1, respectively. For dataset 2, the AUCs are 0.8645, 0.8720, 0.7066 and 0.8547 ± 0.0046 in turn. For dataset 3, the AUCs are 0.9884, 0.9802, 0.8239 and 0.9870 ± 0.0015 in turn. Also, we conducted case studies to further assess the predictive performance of NIRBMSMMA. These results illustrated the proposed model is a useful tool in predicting potential SM-miRNA associations.
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Affiliation(s)
- Jia Qu
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Zihao Song
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Xiaolong Cheng
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Zhibin Jiang
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China
| | - Jie Zhou
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China
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2
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Wang S, Liu T, Ren C, Wu W, Zhao Z, Pang S, Zhang Y. Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm. Brief Bioinform 2023; 24:bbad234. [PMID: 37366591 DOI: 10.1093/bib/bbad234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/28/2023] Open
Abstract
MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM-miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM-miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM-miRNA prediction accuracy. Next, we constructed a heterogeneous SM-miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM-miRNA association prediction.
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Affiliation(s)
- Shudong Wang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Tiyao Liu
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Chuanru Ren
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Wenhao Wu
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Zhiyuan Zhao
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Shanchen Pang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Yuanyuan Zhang
- College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266580, China
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3
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Wang MN, Li Y, Lei LL, Ding DW, Xie XJ. Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion. Front Pharmacol 2023; 14:1132012. [PMID: 36817132 PMCID: PMC9931722 DOI: 10.3389/fphar.2023.1132012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Increasing evidences suggest that miRNAs play a key role in the occurrence and progression of many complex human diseases. Therefore, targeting dysregulated miRNAs with small molecule drugs in the clinical has become a new treatment. Nevertheless, it is high cost and time-consuming for identifying miRNAs-targeted with drugs by biological experiments. Thus, more reliable computational method for identification associations of drugs with miRNAs urgently need to be developed. In this study, we proposed an efficient method, called GNMFDMA, to predict potential associations of drug with miRNA by combining graph Laplacian regularization with non-negative matrix factorization. We first calculated the overall similarity matrices of drugs and miRNAs according to the collected different biological information. Subsequently, the new drug-miRNA association adjacency matrix was reformulated based on the K nearest neighbor profiles so as to put right the false negative associations. Finally, graph Laplacian regularization collaborative non-negative matrix factorization was used to calculate the association scores of drugs with miRNAs. In the cross validation, GNMFDMA obtains AUC of 0.9193, which outperformed the existing methods. In addition, case studies on three common drugs (i.e., 5-Aza-CdR, 5-FU and Gemcitabine), 30, 31 and 34 of the top-50 associations inferred by GNMFDMA were verified. These results reveal that GNMFDMA is a reliable and efficient computational approach for identifying the potential drug-miRNA associations.
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Affiliation(s)
- Mei-Neng Wang
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - Yu Li
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China,*Correspondence: Yu Li,
| | - Li-Lan Lei
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - De-Wu Ding
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - Xue-Jun Xie
- School of Mathematics and Computer Science, Yichun University, Yichun, China
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4
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Li J, Lin H, Wang Y, Li Z, Wu B. Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning. Front Genet 2022; 13:1079053. [PMID: 36531225 PMCID: PMC9755196 DOI: 10.3389/fgene.2022.1079053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2023] Open
Abstract
MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.
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Affiliation(s)
- Jianwei Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
- Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China
| | - Hongxin Lin
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Yinfei Wang
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Zhiguang Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Baoqin Wu
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
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5
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Huang W, Li Z, Kang Y, Ye X, Feng W. Drug Repositioning Based on the Enhanced Message Passing and Hypergraph Convolutional Networks. Biomolecules 2022; 12:1666. [PMID: 36359016 PMCID: PMC9687543 DOI: 10.3390/biom12111666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/03/2022] [Accepted: 11/08/2022] [Indexed: 10/17/2023] Open
Abstract
Drug repositioning, an important method of drug development, is utilized to discover investigational drugs beyond the originally approved indications, expand the application scope of drugs, and reduce the cost of drug development. With the emergence of increasingly drug-disease-related biological networks, the challenge still remains to effectively fuse biological entity data and accurately achieve drug-disease repositioning. This paper proposes a new drug repositioning method named EMPHCN based on enhanced message passing and hypergraph convolutional networks (HGCN). It firstly constructs the homogeneous multi-view information with multiple drug similarity features and then extracts the intra-domain embedding of drugs through the combination of HGCN and channel attention mechanism. Secondly, inter-domain information of known drug-disease associations is extracted by graph convolutional networks combining node and edge embedding (NEEGCN), and a heterogeneous network composed of drugs, proteins and diseases is built as an important auxiliary to enhance the inter-domain message passing of drugs and diseases. Besides, the intra-domain embedding of diseases is also extracted through HGCN. Ultimately, intra-domain and inter-domain embeddings of drugs and diseases are integrated as the final embedding for calculating the drug-disease correlation matrix. Through 10-fold cross-validation on some benchmark datasets, we find that the AUPR of EMPHCN reaches 0.593 (T1) and 0.526 (T2), respectively, and the AUC achieves 0.887 (T1) and 0.961 (T2) respectively, which shows that EMPHCN has an advantage over other state-of-the-art prediction methods. Concerning the new disease association prediction, the AUC of EMPHCN through the five-fold cross-validation reaches 0.806 (T1) and 0.845 (T2), which are 4.3% (T1) and 4.0% (T2) higher than the second best existing methods, respectively. In the case study, EMPHCN also achieves satisfactory results in real drug repositioning for breast carcinoma and Parkinson's disease.
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Affiliation(s)
- Weihong Huang
- School of Informatics Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Zhong Li
- School of Informatics Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Yanlei Kang
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Xinghuo Ye
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Wenming Feng
- Department of General Surgery, The First Affiliated Hospital of Huzhou University, Huzhou 313000, China
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6
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Yu F, Li B, Sun J, Qi J, De Wilde RL, Torres-de la Roche LA, Li C, Ahmad S, Shi W, Li X, Chen Z. PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules. Front Mol Biosci 2022; 9:817294. [PMID: 35386297 PMCID: PMC8979021 DOI: 10.3389/fmolb.2022.817294] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/10/2022] [Indexed: 12/13/2022] Open
Abstract
Background: MicroRNAs (miRNAs) play key roles in a variety of pathological processes by interacting with their specific target mRNAs for translation repression and may function as oncogenes (oncomiRs) or tumor suppressors (TSmiRs). Therefore, a web server that could predict the regulation relations between miRNAs and small molecules is expected to achieve implications for identifying potential therapeutic targets for anti-tumor drug development. Methods: Upon obtaining positive/known small molecule-miRNA regulation pairs from SM2miR, we generated a multitude of high-quality negative/unknown pairs by leveraging similarities between the small molecule structures. Using the pool of the positive and negative pairs, we created the Dataset1 and Dataset2 datasets specific to up-regulation and down-regulation pairs, respectively. Manifold machine learning algorithms were then employed to construct models of predicting up-regulation and down-regulation pairs on the training portion of pairs in Dataset1 and Dataset2, respectively. Prediction abilities of the resulting models were further examined by discovering potential small molecules to regulate oncogenic miRNAs identified from miRNA sequencing data of endometrial carcinoma samples. Results: The random forest algorithm outperformed four machine-learning algorithms by achieving the highest AUC values of 0.911 for the up-regulation model and 0.896 for the down-regulation model on the testing datasets. Moreover, the down-regulation and up-regulation models yielded the accuracy values of 0.91 and 0.90 on independent validation pairs, respectively. In a case study, our model showed highly-reliable results by confirming all top 10 predicted regulation pairs as experimentally validated pairs. Finally, our predicted binding affinities of oncogenic miRNAs and small molecules bore a close resemblance to the lowest binding energy profiles using molecular docking. Predictions of the final model are freely accessible through the PSRR web server at https://rnadrug.shinyapps.io/PSRR/. Conclusion: Our study provides a novel web server that could effectively predict the regulation of miRNAs expression by small molecules.
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Affiliation(s)
- Fanrong Yu
- Department of Obstetrics and Gynecology, Fengxian District Central Hospital, Shanghai Jiao Tong University Affiliated to Sixth People’s Hospital South Campus, Shanghai, China
| | - Bihui Li
- Department of Oncology, The Second Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Jianfeng Sun
- Department of Bioinformatics, Wissenschaftzentrum Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jing Qi
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße, Düsseldorf, Germany
| | - Rudy Leon De Wilde
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
| | | | - Cheng Li
- Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical College of Peking University, Beijing, China
| | - Sajjad Ahmad
- Department of Health and Biological Sciences, Abasyn University, Peshawar, Pakistan
| | - Wenjie Shi
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
| | - Xiqing Li
- Oncology Department, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- *Correspondence: Zihao Chen, ; Xiqing Li,
| | - Zihao Chen
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
- *Correspondence: Zihao Chen, ; Xiqing Li,
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7
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Abdelbaky I, Tayara H, Chong KT. Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders. Pharmaceutics 2021; 14:pharmaceutics14010003. [PMID: 35056899 PMCID: PMC8780428 DOI: 10.3390/pharmaceutics14010003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 12/17/2022] Open
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.
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Affiliation(s)
- Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt;
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
- Correspondence: (H.T.); (K.T.C.); Tel.: +82-63-270-2478 (K.T.C.)
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
- Correspondence: (H.T.); (K.T.C.); Tel.: +82-63-270-2478 (K.T.C.)
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8
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Wang SH, Wang CC, Huang L, Miao LY, Chen X. Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations. Brief Bioinform 2021; 23:6447431. [PMID: 34864865 DOI: 10.1093/bib/bbab500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/25/2021] [Accepted: 11/02/2021] [Indexed: 01/01/2023] Open
Abstract
MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.
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Affiliation(s)
- Shu-Hao Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Lian-Ying Miao
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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9
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Wang CC, Zhu CC, Chen X. Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease. Brief Bioinform 2021; 23:6407727. [PMID: 34676393 DOI: 10.1093/bib/bbab431] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/06/2021] [Accepted: 09/18/2021] [Indexed: 12/12/2022] Open
Abstract
MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM-miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule-MiRNA Association prediction (EKRRSMMA) to uncover potential SM-miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM-miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2'-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM-miRNA associations.
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Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chi-Chi Zhu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
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10
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Chen X, Zhou C, Wang CC, Zhao Y. Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization. Brief Bioinform 2021; 22:6353837. [PMID: 34404088 DOI: 10.1093/bib/bbab328] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/24/2021] [Accepted: 07/26/2021] [Indexed: 12/14/2022] Open
Abstract
Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM-miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM-miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM-miRNA network utilizing miRNA similarity, SM similarity, confirmed SM-miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations.
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Affiliation(s)
- Xing Chen
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
| | - Chi Zhou
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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11
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Winkle M, El-Daly SM, Fabbri M, Calin GA. Noncoding RNA therapeutics - challenges and potential solutions. Nat Rev Drug Discov 2021; 20:629-651. [PMID: 34145432 PMCID: PMC8212082 DOI: 10.1038/s41573-021-00219-z] [Citation(s) in RCA: 734] [Impact Index Per Article: 244.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2021] [Indexed: 02/07/2023]
Abstract
Therapeutic targeting of noncoding RNAs (ncRNAs), such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), represents an attractive approach for the treatment of cancers, as well as many other diseases. Over the past decade, substantial effort has been made towards the clinical application of RNA-based therapeutics, employing mostly antisense oligonucleotides and small interfering RNAs, with several gaining FDA approval. However, trial results have so far been ambivalent, with some studies reporting potent effects whereas others demonstrated limited efficacy or toxicity. Alternative entities such as antimiRNAs are undergoing clinical testing, and lncRNA-based therapeutics are gaining interest. In this Perspective, we discuss key challenges facing ncRNA therapeutics - including issues associated with specificity, delivery and tolerability - and focus on promising emerging approaches that aim to boost their success.
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Affiliation(s)
- Melanie Winkle
- Translational Molecular Pathology, MD Anderson Cancer Center, Texas State University, Houston, TX, USA
| | - Sherien M El-Daly
- Medical Biochemistry Department, Medical Research Division - Cancer Biology and Genetics Laboratory, Centre of Excellence for Advanced Sciences - National Research Centre, Cairo, Egypt
| | - Muller Fabbri
- Cancer Biology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - George A Calin
- Translational Molecular Pathology, MD Anderson Cancer Center, Texas State University, Houston, TX, USA.
- The RNA Interference and Non-codingRNA Center, MD Anderson Cancer Center, Texas State University, Houston, TX, USA.
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12
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Shi W, Chen X, Deng L. A Review of Recent Developments and Progress in Computational Drug Repositioning. Curr Pharm Des 2021; 26:3059-3068. [PMID: 31951162 DOI: 10.2174/1381612826666200116145559] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 01/09/2020] [Indexed: 12/27/2022]
Abstract
Computational drug repositioning is an efficient approach towards discovering new indications for existing drugs. In recent years, with the accumulation of online health-related information and the extensive use of biomedical databases, computational drug repositioning approaches have achieved significant progress in drug discovery. In this review, we summarize recent advancements in drug repositioning. Firstly, we explicitly demonstrated the available data source information which is conducive to identifying novel indications. Furthermore, we provide a summary of the commonly used computing approaches. For each method, we briefly described techniques, case studies, and evaluation criteria. Finally, we discuss the limitations of the existing computing approaches.
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Affiliation(s)
- Wanwan Shi
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xuegong Chen
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
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13
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Sun P, Yang S, Cao Y, Cheng R, Han S. Prediction of Potential Associations Between miRNAs and Diseases Based on Matrix Decomposition. Front Genet 2020; 11:598185. [PMID: 33304393 PMCID: PMC7701300 DOI: 10.3389/fgene.2020.598185] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/22/2020] [Indexed: 01/06/2023] Open
Abstract
It is known that miRNA plays an increasingly important role in many physiological processes. Disease-related miRNAs could be potential biomarkers for clinical diagnosis, prognosis, and treatment. Therefore, accurately inferring potential miRNAs related to diseases has become a hot topic in the bioinformatics community recently. In this study, we proposed a mathematical model based on matrix decomposition, named MFMDA, to identify potential miRNA-disease associations by integrating known miRNA and disease-related data, similarities between miRNAs and between diseases. We also compared MFMDA with some of the latest algorithms in several established miRNA disease databases. MFMDA reached an AUC of 0.9061 in the fivefold cross-validation. The experimental results show that MFMDA effectively infers novel miRNA-disease associations. In addition, we conducted case studies by applying MFMDA to three types of high-risk human cancers. While most predicted miRNAs are confirmed by external databases of experimental literature, we also identified a few novel disease-related miRNAs for further experimental validation.
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Affiliation(s)
- Pengcheng Sun
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shuyan Yang
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ye Cao
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Rongjie Cheng
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shiyu Han
- Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
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14
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Estrogen-dependent MicroRNA-504 Expression and Related Baroreflex Afferent Neuroexcitation via Negative Regulation on KCNMB4 and KCa1.1 β4-subunit Expression. Neuroscience 2020; 442:168-182. [DOI: 10.1016/j.neuroscience.2020.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 07/01/2020] [Accepted: 07/01/2020] [Indexed: 11/21/2022]
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15
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Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform 2020; 12:46. [PMID: 33431024 PMCID: PMC7374666 DOI: 10.1186/s13321-020-00450-7] [Citation(s) in RCA: 139] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/13/2020] [Indexed: 01/13/2023] Open
Abstract
Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.
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Affiliation(s)
- Tamer N Jarada
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Jon G Rokne
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.
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16
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Shen C, Luo J, Lai Z, Ding P. Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge. J Chem Inf Model 2020; 60:4085-4097. [DOI: 10.1021/acs.jcim.0c00244] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Zihan Lai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang 421001, China
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17
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Liu F, Peng L, Tian G, Yang J, Chen H, Hu Q, Liu X, Zhou L. Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk. Front Bioeng Biotechnol 2020; 8:131. [PMID: 32258003 PMCID: PMC7090022 DOI: 10.3389/fbioe.2020.00131] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/10/2020] [Indexed: 12/05/2022] Open
Abstract
Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.
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Affiliation(s)
- Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing, China
| | | | - Hui Chen
- College of Chemical Engineering, Xiangtan University, Xiangtan, China
| | - Qi Hu
- Xiangya Second Hospital, Central South University, Changsha, Hunan, China
| | - Xiaojun Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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18
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Zhao Y, Chen X, Yin J, Qu J. SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association. RNA Biol 2019; 17:281-291. [PMID: 31739716 DOI: 10.1080/15476286.2019.1694732] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of experimental methods. Considering the shortcomings of the previous models, such as the prediction accuracy of some models is low or some cannot be applied for new SMs (miRNAs), we developed a novel model named Symmetric Nonnegative Matrix Factorization for Small Molecule-MiRNA Association prediction (SNMFSMMA). Different from some models directly applying the integrated similarities, SNMFSMMA first performed matrix decomposition on the integrated similarity matrixes, and calculated the Kronecker product of the new integrated similarity matrixes to obtain the SM-miRNA pair similarity. Further, we applied regularized least square to obtain the mapping function of the SM-miRNA pairs to the associated probabilities by minimizing the objective function. On the basis of Dataset 1 and 2 extracted from SM2miR v1.0 database, we implemented global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold cross-validation to evaluate the prediction performance. Finally, the AUC values obtained by SNMFSMMA in these validation reached 0.9711 (0.8895), 0.9698 (0.8884), 0.8329 (0.7651) and 0.9644 ± 0.0035 (0.8814 ± 0.0033) based on Dataset 1 (Dataset 2), respectively. In the first case study, 5 of the top 10 associations predicted were confirmed. In the second, 7 and 8 of the top 10 predicted miRNAs related with 5-FU and 5-Aza-2'-deoxycytidine were confirmed. These results demonstrated the reliable predictive power of SNMFSMMA.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
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19
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Wang CC, Chen X. A Unified Framework for the Prediction of Small Molecule–MicroRNA Association Based on Cross-Layer Dependency Inference on Multilayered Networks. J Chem Inf Model 2019; 59:5281-5293. [DOI: 10.1021/acs.jcim.9b00667] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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20
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To KKW, Fong W, Tong CWS, Wu M, Yan W, Cho WCS. Advances in the discovery of microRNA-based anticancer therapeutics: latest tools and developments. Expert Opin Drug Discov 2019; 15:63-83. [PMID: 31739699 DOI: 10.1080/17460441.2020.1690449] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Introduction: MicroRNAs (miRNAs) are small endogenous non-coding RNAs that repress the expression of their target genes by reducing mRNA stability and/or inhibiting translation. miRNAs are known to be aberrantly regulated in cancers. Modulators of miRNA (mimics and antagonists) have emerged as novel therapeutic tools for cancer treatment.Areas covered: This review summarizes the various strategies that have been applied to correct the dysregulated miRNA in cancer cells. The authors also discuss the recent advances in the technical development and preclinical/clinical evaluation of miRNA-based therapeutic agents.Expert opinion: Application of miRNA-based therapeutics for cancer treatment is appealing because they are able to modulate multiple dysregulated genes and/or signaling pathways in cancer cells. Major obstacles hindering their clinical development include drug delivery, off-target effects, efficacious dose determination, and safety. Tumor site-specific delivery of novel miRNA therapeutics may help to minimize off-target effects and toxicity. Combination of miRNA therapeutics with other anticancer treatment modalities could provide a synergistic effect, thus allowing the use of lower dose, minimizing off-target effects, and improving the overall safety profile in cancer patients. It is critical to identify individual miRNAs with cancer type-specific and context-specific regulation of oncogenes and tumor-suppressor genes in order to facilitate the precise use of miRNA anticancer therapeutics.
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Affiliation(s)
- Kenneth K W To
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie Fong
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Christy W S Tong
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Mingxia Wu
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Wei Yan
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - William C S Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
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21
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Identification and profiling of microRNAs expressed in oral buccal mucosa squamous cell carcinoma of Chinese hamster. Sci Rep 2019; 9:15616. [PMID: 31666604 PMCID: PMC6821846 DOI: 10.1038/s41598-019-52197-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 10/13/2019] [Indexed: 12/13/2022] Open
Abstract
MicroRNAs are known to play essential role in the gene expression regulation in cancer. In our research, next-generation sequencing technology was applied to explore the abnormal miRNA expression of oral squamous cell carcinoma (OSCC) in Chinese hamster. A total of 3 novel miRNAs (Novel-117, Novel-118, and Novel-135) and 11 known miRNAs (crg-miR-130b-3p, crg-miR-142-5p, crg-miR-21-3p, crg-miR-21-5p, crg-miR-542-3p, crg-miR-486-3p, crg-miR-499-5p, crg-miR-504, crg-miR-34c-5p, crg-miR-34b-5p and crg-miR-34c-3p) were identified. We conducted functional analysis, finding that 340 biological processes, 47 cell components, 46 molecular functions were associated with OSCC. Meanwhile the gene expression of Caspase-9, Caspase-3, Bax, and Bcl-2 were determined by qRT-PCR and the protein expression of PTEN and p-AKT by immunohistochemistry. Our research proposed further insights to the profiles of these miRNAs and provided a basis for investigating the regulatory mechanisms involved in oral cancer research.
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22
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Chen X, Xie D, Zhao Q, You ZH. MicroRNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2019; 20:515-539. [PMID: 29045685 DOI: 10.1093/bib/bbx130] [Citation(s) in RCA: 392] [Impact Index Per Article: 78.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 08/13/2017] [Indexed: 12/22/2022] Open
Abstract
Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Di Xie
- School of Mathematics, Liaoning University
| | - Qi Zhao
- School of Mathematics, Liaoning University
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science
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23
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Xie WB, Yan H, Zhao XM. EmDL: Extracting miRNA-Drug Interactions from Literature. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1722-1728. [PMID: 28692985 DOI: 10.1109/tcbb.2017.2723394] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The microRNAs (miRNAs), regulators of post-transcriptional processes, have been found to affect the efficacy of drugs by regulating the biological processes in which the target proteins of drugs may be involved. For example, some drugs develop resistance when certain miRNAs are overexpressed. Therefore, identifying miRNAs that affect drug effects can help understand the mechanisms of drug actions and design more efficient drugs. Although some computational approaches have been developed to predict miRNA-drug associations, such associations rarely provide explicit information about which miRNAs and how they affect drug efficacy. On the other hand, there are rich information about which miRNAs affect the efficacy of which drugs in the literature. In this paper, we present a novel text mining approach, named as EmDL (Extracting miRNA-Drug interactions from Literature), to extract the relationships of miRNAs affecting drug efficacy from literature. Benchmarking on the drug-miRNA interactions manually extracted from MEDLINE and PubMed Central, EmDL outperforms traditional text mining approaches as well as other popular methods for predicting drug-miRNA associations. Specifically, EmDL can effectively identify the sentences that describe the relationships of miRNAs affecting drug effects. The drug-miRNA interactome presented here can help understand how miRNAs affect drug effects and provide insights into the mechanisms of drug actions. In addition, with the information about drug-miRNA interactions, more effective drugs or combinatorial strategies can be designed in the future. The data used here can be accessed at http://mtd.comp-sysbio.org/.
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24
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Yin J, Chen X, Wang CC, Zhao Y, Sun YZ. Prediction of Small Molecule–MicroRNA Associations by Sparse Learning and Heterogeneous Graph Inference. Mol Pharm 2019; 16:3157-3166. [DOI: 10.1021/acs.molpharmaceut.9b00384] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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25
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Musa A, Tripathi S, Dehmer M, Yli-Harja O, Kauffman SA, Emmert-Streib F. Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks. Sci Rep 2019; 9:7849. [PMID: 31127155 PMCID: PMC6534546 DOI: 10.1038/s41598-019-44291-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 05/08/2019] [Indexed: 02/01/2023] Open
Abstract
Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.
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Affiliation(s)
- Aliyu Musa
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
| | - Shailesh Tripathi
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, 4400, Steyr, Austria
| | - Matthias Dehmer
- Department for Biomedical Computer Science and Mechatronics, UMIT - The Health and Lifesciences University, Eduard Wallnoefer Zentrum 1, 6060, Hall in Tyrol, Austria
- College of Computer and Control Engineering, Nankai University, Tianjin, 300350, P.R. China
- Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, 4400, Steyr, Austria
| | - Olli Yli-Harja
- Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Computational Systems Biology Lab, Tampere University of Technology, Korkeakoulunkatu 10, 33720, Tampere, Finland
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | | | - Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.
- Institute of Biosciences and Medical Technology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.
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26
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Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases. Artif Intell Med 2019; 96:80-92. [DOI: 10.1016/j.artmed.2019.03.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 02/24/2019] [Accepted: 03/05/2019] [Indexed: 01/09/2023]
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27
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Wang CC, Chen X, Qu J, Sun YZ, Li JQ. RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations. J Chem Inf Model 2019; 59:1668-1679. [PMID: 30840454 DOI: 10.1021/acs.jcim.9b00129] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
More and more studies found that many complex human diseases occur accompanied by aberrant expression of microRNAs (miRNAs). Small molecule (SM) drugs have been utilized to treat complex human diseases by affecting the expression of miRNAs. Several computational methods were proposed to infer underlying associations between SMs and miRNAs. In our study, we proposed a new calculation model of random forest based small molecule-miRNA association prediction (RFSMMA) which was based on the known SM-miRNA associations in the SM2miR database. RFSMMA utilized the similarity of SMs and miRNAs as features to represent SM-miRNA pairs and further implemented the machine learning algorithm of random forest to train training samples and obtain a prediction model. In RFSMMA, integrating multiple kinds of similarity can avoid the bias of single similarity and choosing more reliable features from original features can represent SM-miRNA pairs more accurately. We carried out cross validations to assess predictive accuracy of RFSMMA. As a result, RFSMMA acquired AUCs of 0.9854, 0.9839, 0.7052, and 0.9917 ± 0.0008 under global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV, and 5-fold cross validation, respectively, under data set 1. Based on data set 2, RFSMMA obtained AUCs of 0.8456, 0.8463, 0.6653, and 0.8389 ± 0.0033 under four cross validations according to the order mentioned above. In addition, we implemented a case study on three common SMs, namely, 5-fluorouracil, 17β-estradiol, and 5-aza-2'-deoxycytidine. Among the top 50 associated miRNAs of these three SMs predicted by RFSMMA, 31, 32, and 28 miRNAs were verified, respectively. Therefore, RFSMMA is shown to be an effective and reliable tool for identifying underlying SM-miRNA associations.
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Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering , China University of Mining and Technology , Xuzhou 221116 , China
| | - Xing Chen
- School of Information and Control Engineering , China University of Mining and Technology , Xuzhou 221116 , China
| | - Jia Qu
- School of Information and Control Engineering , China University of Mining and Technology , Xuzhou 221116 , China
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering , Shenzhen University , Shenzhen 518060 , China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering , Shenzhen University , Shenzhen 518060 , China
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28
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Zhou X, Dai E, Song Q, Ma X, Meng Q, Jiang Y, Jiang W. In silico drug repositioning based on drug-miRNA associations. Brief Bioinform 2019; 21:498-510. [DOI: 10.1093/bib/bbz012] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 12/14/2018] [Accepted: 01/11/2019] [Indexed: 02/06/2023] Open
Abstract
Abstract
Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Furthermore, a comprehensive drug–drug interaction network was constructed based on curated drug-miRNA associations. Through random network comparison, topological structure analysis and network module extraction, we found that the closely linked drugs in the network tend to treat the same diseases. Additionally, the curated drug–disease relationships (from the CTD) and random walk with restarts algorithm were utilized on the drug–drug interaction network to identify the potential drugs for a given disease. Both internal validation (leave-one-out cross-validation) and external validation (independent drug–disease data set from the ChEMBL) demonstrated the effectiveness of the proposed approach. Finally, by integrating drug-miRNA and miRNA-disease information, we also explain the modes of action of drugs in the view of miRNA regulation. In summary, our work could determine novel and credible drug indications and offer novel insights and valuable perspectives for drug repositioning.
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Affiliation(s)
- Xu Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Enyu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Qian Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Xueyan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Qianqian Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Wei Jiang
- Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China
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29
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Qu J, Chen X, Sun YZ, Zhao Y, Cai SB, Ming Z, You ZH, Li JQ. In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm. MOLECULAR THERAPY-NUCLEIC ACIDS 2018; 14:274-286. [PMID: 30654189 PMCID: PMC6348698 DOI: 10.1016/j.omtn.2018.12.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/15/2018] [Accepted: 12/04/2018] [Indexed: 01/27/2023]
Abstract
Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.
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Affiliation(s)
- Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Shu-Bin Cai
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Zhong Ming
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi 830011, China.
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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30
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Chen X, Guan NN, Sun YZ, Li JQ, Qu J. MicroRNA-small molecule association identification: from experimental results to computational models. Brief Bioinform 2018; 21:47-61. [PMID: 30325405 DOI: 10.1093/bib/bby098] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 09/07/2018] [Accepted: 09/07/2018] [Indexed: 12/14/2022] Open
Abstract
Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Na-Na Guan
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
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31
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Guan NN, Sun YZ, Ming Z, Li JQ, Chen X. Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction. Front Pharmacol 2018; 9:1152. [PMID: 30374302 PMCID: PMC6196296 DOI: 10.3389/fphar.2018.01152] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 09/24/2018] [Indexed: 11/13/2022] Open
Abstract
MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17β-Estradiol and 5-Aza-2'-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.
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Affiliation(s)
- Na-Na Guan
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Zhong Ming
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.,National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
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32
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Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion. Molecules 2018; 23:molecules23102439. [PMID: 30249984 PMCID: PMC6222788 DOI: 10.3390/molecules23102439] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 09/14/2018] [Accepted: 09/18/2018] [Indexed: 12/11/2022] Open
Abstract
Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.
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33
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Prediction of Non-coding RNAs as Drug Targets. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1094:109-115. [DOI: 10.1007/978-981-13-0719-5_11] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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34
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Qu J, Chen X, Sun YZ, Li JQ, Ming Z. Inferring potential small molecule-miRNA association based on triple layer heterogeneous network. J Cheminform 2018; 10:30. [PMID: 29943160 PMCID: PMC6020102 DOI: 10.1186/s13321-018-0284-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 06/19/2018] [Indexed: 12/12/2022] Open
Abstract
Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule–MiRNA association prediction (TLHNSMMA) to uncover potential SM–miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM–miRNA associations and miRNA–disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 ± 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 ± 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM–miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM–miRNA associations.
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Affiliation(s)
- Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Ya-Zhou Sun
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China.,College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Jian-Qiang Li
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China.,College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zhong Ming
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China.,College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
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35
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018; 19:506-523. [PMID: 28069634 PMCID: PMC5952941 DOI: 10.1093/bib/bbw112] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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36
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Musa A, Ghoraie LS, Zhang SD, Glazko G, Yli-Harja O, Dehmer M, Haibe-Kains B, Emmert-Streib F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018. [PMID: 28069634 DOI: 10.1093/bib] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.
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Affiliation(s)
- Aliyu Musa
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Laleh Soltan Ghoraie
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Shu-Dong Zhang
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry BT47 6SB, Northern Ireland, UK
| | - Galina Glazko
- University of Rochester Department of Biostatistics and Computational Biology, Rochester, New York 14642, USA
| | - Olli Yli-Harja
- Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria
| | - Benjamin Haibe-Kains
- Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Ontario Institute of Cancer Research, Toronto, ON, Canada
| | - Frank Emmert-Streib
- Predictive Medicine and Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
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37
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Wang J, Meng F, Dai E, Yang F, Wang S, Chen X, Yang L, Wang Y, Jiang W. Identification of associations between small molecule drugs and miRNAs based on functional similarity. Oncotarget 2018; 7:38658-38669. [PMID: 27232942 PMCID: PMC5122418 DOI: 10.18632/oncotarget.9577] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 05/08/2016] [Indexed: 12/18/2022] Open
Abstract
MicroRNAs (miRNAs) are a class of small non-coding RNA molecules that regulate gene expression at post-transcriptional level. Increasing evidences show aberrant expression of miRNAs in varieties of diseases. Targeting the dysregulated miRNAs with small molecule drugs has become a novel therapy for many human diseases, especially cancer. Here, we proposed a novel computational approach to identify associations between small molecules and miRNAs based on functional similarity of differentially expressed genes. At the significance level of p < 0.01, we constructed the small molecule and miRNA functional similarity network involving 111 small molecules and 20 miRNAs. Moreover, we also predicted associations between drugs and diseases through integrating our identified small molecule-miRNA associations with experimentally validated disease related miRNAs. As a result, we identified 2265 associations between FDA approved drugs and diseases, in which ~35% associations have been validated by comprehensive literature reviews. For breast cancer, we identified 19 potential drugs, in which 12 drugs were supported by previous studies. In addition, we performed survival analysis for the patients from TCGA and GEO database, which indicated that the associated miRNAs of 4 drugs might be good prognosis markers in breast cancer. Collectively, this study proposed a novel approach to predict small molecule and miRNA associations based on functional similarity, which may pave a new way for miRNA-targeted therapy and drug repositioning.
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Affiliation(s)
- Jing Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Fanlin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - EnYu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Yuwen Wang
- The 2nd Affiliated Hospital, Harbin Medical University, Harbin 150081, P. R. China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
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38
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Cheng F, Hong H, Yang S, Wei Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief Bioinform 2017; 18:682-697. [PMID: 27296652 DOI: 10.1093/bib/bbw051] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Indexed: 12/12/2022] Open
Abstract
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.
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39
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Chen H, Zhang Z, Peng W. miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships. Sci Rep 2017; 7:15921. [PMID: 29162848 PMCID: PMC5698443 DOI: 10.1038/s41598-017-15716-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 10/31/2017] [Indexed: 01/10/2023] Open
Abstract
Revealing the cause-and-effect mechanism behind drug-disease relationships remains a challenging task. Recent studies suggested that drugs can target microRNAs (miRNAs) and alter their expression levels. In the meanwhile, the inappropriate expression of miRNAs will lead to various diseases. Therefore, targeting specific miRNAs by small-molecule drugs to modulate their activities provides a promising approach to human disease treatment. However, few studies attempt to discover drug-disease causal relationships through the molecular level of miRNAs. Here, we developed a miRNA-based inference method miRDDCR to comprehensively predict drug-disease causal relationships. We first constructed a three-layer drug-miRNA-disease heterogeneous network by combining similarity measurements, existing drug-miRNA associations and miRNA-disease associations. Then, we extended the algorithm of Random Walk to the three-layer heterogeneous network and ranked the potential indications for drugs. Leave-one-out cross-validations and case studies demonstrated that our method miRDDCR can achieve excellent prediction power. Compared with related methods, our causality discovery-based algorithm showed superior prediction ability and highlighted the molecular basis miRNAs, which can be used to assist in the experimental design for drug development and disease treatment. Finally, comprehensively inferred drug-disease causal relationships were released for further studies.
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Affiliation(s)
- Hailin Chen
- School of Software, East China Jiaotong University, Nanchang, China.
| | - Zuping Zhang
- School of Information Science and Engineering, Central South University, Changsha, China
| | - Wei Peng
- Computer Center of Kunming University of Science and Technology, Kunming, China
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40
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Yang Q, Wang S, Dai E, Zhou S, Liu D, Liu H, Meng Q, Jiang B, Jiang W. Pathway enrichment analysis approach based on topological structure and updated annotation of pathway. Brief Bioinform 2017; 20:168-177. [DOI: 10.1093/bib/bbx091] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Indexed: 12/31/2022] Open
Affiliation(s)
- Qian Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
| | - Enyu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
| | - Shunheng Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
| | - Dianming Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
| | - Haizhou Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
| | - Qianqian Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
| | - Bin Jiang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, People’s Republic of China
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China
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41
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Chen X, Xie WB, Xiao PP, Zhao XM, Yan H. mTD: A database of microRNAs affecting therapeutic effects of drugs. J Genet Genomics 2017; 44:269-271. [DOI: 10.1016/j.jgg.2017.04.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Revised: 04/24/2017] [Accepted: 04/28/2017] [Indexed: 11/26/2022]
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42
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Ni Y, Song C, Jin S, Chen Z, Ni M, Han L, Wu J, Jin Y. Gene set enrichment analysis: A genome-wide expression profile-based strategy for discovering functional microRNA-disease relationships. J Int Med Res 2017; 46:596-611. [PMID: 28436302 PMCID: PMC5971487 DOI: 10.1177/0300060517693719] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Objective To explore stable and functional microRNA (miRNA)–disease relationships using a genome-wide expression profile pattern matching strategy. Methods We applied the ranked microarray pattern matching strategy Gene Set Enrichment Analysis to identify miRNA permutations with similar expression patterns to diseases. We also used quantitative reverse transcription PCR to validate the predicted expression levels of miRNAs in three diseases: inflammatory bowel disease (IBD), oesophageal cancer, and colorectal cancer. Results We found that hsa-miR-200 c was upregulated more than 40-fold in oesophageal cancer. The expression of miR-16 and miR-124 was not consistently upregulated in IBD or colorectal cancer. Conclusions Our results suggest that this expression profile matching strategy can be used to identify functional miRNA–disease relationships.
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Affiliation(s)
- Yin Ni
- 1 Department of Intensive Care Unit, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Caiyun Song
- 2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shuqing Jin
- 2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhoufeng Chen
- 2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ming Ni
- 3 Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Lu Han
- 3 Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Jiansheng Wu
- 2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yin Jin
- 2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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43
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Enhanced Response of Metformin towards the Cancer Cells due to Synergism with Multi-walled Carbon Nanotubes in Photothermal Therapy. Sci Rep 2017; 7:1071. [PMID: 28432330 PMCID: PMC5430827 DOI: 10.1038/s41598-017-01118-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 03/27/2017] [Indexed: 12/21/2022] Open
Abstract
Converging evidence from laboratory models pointed that the widely used antidiabetic drug metformin has direct effects on cancer cells. Thus far, relatively little attention has been addressed to the drug exposures used experimentally relative to those achievable clinically. Here, we demonstrated that metformin loaded on carbon nanotubes under near-infrared (NIR) irradiation led to the remarkably enhancement in response towards cancer cells. The dose of metformin has reduced to only 1/280 of typical doses in monotherapy (35: 10 000–30 000 µM) where the realization of metformin in conventional antidiabetic doses for cancer therapies becomes possible. The heat generated from carbon nanotubes upon NIR irradiation has mediated a strong and highly localized hyperthermia-like condition that facilitated the enhancement. Our work highlight the promise of using highly localized heating from carbon nanotubes to intensify the efficacy of metformin for potential cancer therapies.
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44
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Gligorijević V, Malod-Dognin N, Pržulj N. Integrative methods for analyzing big data in precision medicine. Proteomics 2016; 16:741-58. [PMID: 26677817 DOI: 10.1002/pmic.201500396] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 11/16/2015] [Accepted: 12/09/2015] [Indexed: 12/19/2022]
Abstract
We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of "Big Data" in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.
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Affiliation(s)
| | | | - Nataša Pržulj
- Department of Computing, Imperial College London, London, UK
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45
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Chen X, Shi H, Yang F, Yang L, Lv Y, Wang S, Dai E, Sun D, Jiang W. Large-scale identification of adverse drug reaction-related proteins through a random walk model. Sci Rep 2016; 6:36325. [PMID: 27805066 PMCID: PMC5090865 DOI: 10.1038/srep36325] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 10/13/2016] [Indexed: 12/19/2022] Open
Abstract
Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life quality of patients. The identification of ADRs during the early phases of drug development is an important task. Therefore, predicting potential protein targets eliciting ADRs is essential for understanding the pathogenesis of ADRs. In this study, we proposed a computational algorithm,Integrated Network for Protein-ADR relations (INPADR), to infer potential protein-ADR relations based on an integrated network. First, the integrated network was constructed by connecting the protein-protein interaction network and the ADR similarity network using known protein-ADR relations. Then, candidate protein-ADR relations were further prioritized by performing a random walk with restart on this integrated network. Leave-one-out cross validation was used to evaluate the ability of the INPADR. An AUC of 0.8486 was obtained, which was a significant improvement compared to previous methods. We also applied the INPADR to two ADRs to evaluate its accuracy. The results suggested that the INPADR is capable of finding novel protein-ADR relations. This study provides new insight to our understanding of ADRs. The predicted ADR-related proteins will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during the early phases of drug development.
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Affiliation(s)
- Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hongbo Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yingli Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Enyu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Dianjun Sun
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, 150081, China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
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46
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Qu Z, Yuan CH, Yin CQ, Guan Q, Chen H, Wang FB. Meta-analysis of the prognostic value of abnormally expressed lncRNAs in hepatocellular carcinoma. Onco Targets Ther 2016. [PMID: 27574455 DOI: 10.2147/ott] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Many long noncoding RNAs (lncRNAs) have been reported to be abnormally expressed in hepatocellular carcinoma (HCC), and may have the potential to serve as prognostic markers. In this study, a meta-analysis was conducted to systematically evaluate the prognostic value of various lncRNAs in HCC. Eligible literatures were systematically collected from PubMed, Embase, Web of Science, and Cochrane Library (up to December 30, 2015). The main outcomes including overall survival, relapse-free survival, and disease-free survival were analyzed. Pooled hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated using random- or fixed-effects models. A total of 2,991 patients with HCC in People's Republic of China from 27 studies were included in the analysis. The level of lncRNAs showed a significant association with clinical outcomes. Abnormally elevated lncRNA transcription level predicted poor overall survival (HR: 1.68, 95% CI: 1.20-2.34, P=0.002; I (2)=75.5%, P=0.000) and relapse-free survival (HR: 2.08, 95% CI: 1.65-2.61, P<0.001; I (2)=24.0%, P=0.215), while no association was observed with disease-free survival of HCC patients (HR: 1.39, 95% CI: 0.51-3.78, P=0.524; I (2)=81.3%, P=0.005). Subgroup analysis further showed that lncRNA transcription level was significantly associated with tumor size (relative risk [RR]: 1.19, 95% CI: 1.01-1.39, P=0.035), microvascular invasion (RR: 1.44, 95% CI: 1.10-1.89, P=0.009), and portal vein tumor thrombus (RR: 1.50, 95% CI: 1.03-2.20, P=0.036). Publication bias and sensitivity analysis further confirmed the stability of our results. Our present meta-analysis indicates that abnormal lncRNA transcription level may serve as a promising indicator for prognostic evaluation of patients with HCC in People's Republic of China.
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Affiliation(s)
- Zhen Qu
- Department of Laboratory Medicine and Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University
| | - Chun-Hui Yuan
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, People's Republic of China
| | - Chang-Qing Yin
- Department of Laboratory Medicine and Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University
| | - Qing Guan
- Department of Immunology, School of Basic Medical Sciences, Wuhan University, Wuhan, People's Republic of China
| | - Hao Chen
- Department of Laboratory Medicine and Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University
| | - Fu-Bing Wang
- Department of Laboratory Medicine and Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University
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47
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Meng F, Wang J, Dai E, Yang F, Chen X, Wang S, Yu X, Liu D, Jiang W. Psmir: a database of potential associations between small molecules and miRNAs. Sci Rep 2016; 6:19264. [PMID: 26759061 PMCID: PMC4713048 DOI: 10.1038/srep19264] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 12/10/2015] [Indexed: 12/29/2022] Open
Abstract
miRNAs are key post-transcriptional regulators of many essential biological processes, and their dysregulation has been validated in almost all human cancers. Restoring aberrantly expressed miRNAs might be a novel therapeutics. Recently, many studies have demonstrated that small molecular compounds can affect miRNA expression. Thus, prediction of associations between small molecules and miRNAs is important for investigation of miRNA-targeted drugs. Here, we analyzed 39 miRNA-perturbed gene expression profiles, and then calculated the similarity of transcription responses between miRNA perturbation and drug treatment to predict drug-miRNA associations. At the significance level of 0.05, we obtained 6501 candidate associations between 1295 small molecules and 25 miRNAs, which included 624 FDA approved drugs. Finally, we constructed the Psmir database to store all potential associations and the related materials. In a word, Psmir served as a valuable resource for dissecting the biological significance in small molecules’ effects on miRNA expression, which will facilitate developing novel potential therapeutic targets or treatments for human cancers. Psmir is supported by all major browsers, and is freely available at http://www.bio-bigdata.com/Psmir/.
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Affiliation(s)
- Fanlin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Jing Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Enyu Dai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Xuexin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Dianming Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, P. R. China
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Wen D, Danquah M, Chaudhary AK, Mahato RI. Small molecules targeting microRNA for cancer therapy: Promises and obstacles. J Control Release 2015; 219:237-247. [PMID: 26256260 DOI: 10.1016/j.jconrel.2015.08.011] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 07/20/2015] [Accepted: 08/04/2015] [Indexed: 02/06/2023]
Abstract
Aberrant expression of miRNAs is critically implicated in cancer initiation and progression. Therapeutic approaches focused on regulating miRNAs are therefore a promising approach for treating cancer. Antisense oligonucleotides, miRNA sponges, and CRISPR/Cas9 genome editing systems are being investigated as tools for regulating miRNAs. Despite the accruing insights in the use of these tools, delivery concerns have mitigated clinical application of such systems. In contrast, little attention has been given to the potential of small molecules to modulate miRNA expression for cancer therapy. In these years, many researches proved that small molecules targeting cancer-related miRNAs might have greater potential for cancer treatment. Small molecules targeting cancer related miRNAs showed significantly promising results in different cancer models. However, there are still several obstacles hindering the progress and clinical application in this area. This review discusses the development, mechanisms and application of small molecules for modulating oncogenic miRNAs (oncomiRs). Attention has also been given to screening technologies and perspectives aimed to facilitate clinical translation for small molecule-based miRNA therapeutics.
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Affiliation(s)
- Di Wen
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, 986025 Nebraska Medical Center, Omaha, NE 68198-6025, USA
| | - Michael Danquah
- Department of Pharmaceutical Sciences, Chicago State University, 9501 South King Drive., Chicago, IL 60628, USA
| | - Amit Kumar Chaudhary
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, 986025 Nebraska Medical Center, Omaha, NE 68198-6025, USA
| | - Ram I Mahato
- Department of Pharmaceutical Sciences, University of Nebraska Medical Center, 986025 Nebraska Medical Center, Omaha, NE 68198-6025, USA.
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49
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Lv Y, Wang S, Meng F, Yang L, Wang Z, Wang J, Chen X, Jiang W, Li Y, Li X. Identifying novel associations between small molecules and miRNAs based on integrated molecular networks. Bioinformatics 2015. [DOI: 10.1093/bioinformatics/btv417] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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50
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Integration of a prognostic gene module with a drug sensitivity module to identify drugs that could be repurposed for breast cancer therapy. Comput Biol Med 2015; 61:163-71. [DOI: 10.1016/j.compbiomed.2014.12.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 12/20/2014] [Accepted: 12/22/2014] [Indexed: 11/22/2022]
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