1
|
Rashid G, Khan NA, Elsori D, Youness RA, Hassan H, Siwan D, Seth N, Kamal MA, Rizvi S, Babker AM, Hafez W. miRNA expression in PCOS: unveiling a paradigm shift toward biomarker discovery. Arch Gynecol Obstet 2024; 309:1707-1723. [PMID: 38316651 DOI: 10.1007/s00404-024-07379-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 01/07/2024] [Indexed: 02/07/2024]
Abstract
Polycystic ovary syndrome (PCOS) is a complex endocrine disorder that affects a substantial percentage of women, estimated at around 9-21%. This condition can lead to anovulatory infertility in women of childbearing age and is often accompanied by various metabolic disturbances, including hyperandrogenism, insulin resistance, obesity, type-2 diabetes, and elevated cholesterol levels. The development of PCOS is influenced by a combination of epigenetic alterations, genetic mutations, and changes in the expression of non-coding RNAs, particularly microRNAs (miRNAs). MicroRNAs, commonly referred to as non-coding RNAs, are approximately 22 nucleotides in length and primarily function in post-transcriptional gene regulation, facilitating mRNA degradation and repressing translation. Their dynamic expression in different cells and tissues contributes to the regulation of various biological and cellular pathways. As a result, they have become pivotal biomarkers for various diseases, including PCOS, demonstrating intricate associations with diverse health conditions. The aberrant expression of miRNAs has been detected in the serum of women with PCOS, with overexpression and dysregulation of these miRNAs playing a central role in the atypical expression of endocrine hormones linked to PCOS. This review takes a comprehensive approach to explore the upregulation and downregulation of various miRNAs present in ovarian follicular cells, granulosa cells, and theca cells of women diagnosed with PCOS. Furthermore, it discusses the potential for a theragnostic approach using miRNAs to better understand and manage PCOS.
Collapse
Affiliation(s)
- Gowhar Rashid
- Department of Medical Lab Technology, Amity Medical School, Amity University Haryana, Gurugram, India.
| | - Nihad Ashraf Khan
- Department of Biosciences, Faculty of Natural Sciences, Jamia Millia Islamia, Delhi, 110025, India
| | | | - Rana A Youness
- Biology and Biochemistry Department, Faculty of Biotechnology, German International University, Cairo, Egypt
| | - Homa Hassan
- Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, New Delhi, India
| | - Deepali Siwan
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University, Delhi, 110017, India
| | - Namrata Seth
- Department of Biotechnology, Indian Institute of Science and Technology, Bhopal, 462066, India
| | - Mohammad Azhar Kamal
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Saliha Rizvi
- Department of Biotechnology, Era University, Lucknow, India
| | - Asaad Ma Babker
- Department of Medical Laboratory Sciences, Gulf Medical University, Ajman, United Arab Emirates
| | - Wael Hafez
- The Medical Research Division, Department of Internal Medicine, the National Research Centre, Cairo, Egypt
| |
Collapse
|
2
|
Xu P, Li C, Yuan J, Bao Z, Liu W. Predict lncRNA-drug associations based on graph neural network. Front Genet 2024; 15:1388015. [PMID: 38737125 PMCID: PMC11082279 DOI: 10.3389/fgene.2024.1388015] [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: 02/19/2024] [Accepted: 04/05/2024] [Indexed: 05/14/2024] Open
Abstract
LncRNAs are an essential type of non-coding RNAs, which have been reported to be involved in various human pathological conditions. Increasing evidence suggests that drugs can regulate lncRNAs expression, which makes it possible to develop lncRNAs as therapeutic targets. Thus, developing in-silico methods to predict lncRNA-drug associations (LDAs) is a critical step for developing lncRNA-based therapies. In this study, we predict LDAs by using graph convolutional networks (GCN) and graph attention networks (GAT) based on lncRNA and drug similarity networks. Results show that our proposed method achieves good performance (average AUCs > 0.92) on five datasets. In addition, case studies and KEGG functional enrichment analysis further prove that the model can effectively identify novel LDAs. On the whole, this study provides a deep learning-based framework for predicting novel LDAs, which will accelerate the lncRNA-targeted drug development process.
Collapse
Affiliation(s)
- Peng Xu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Chuchu Li
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Jiaqi Yuan
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Zhenshen Bao
- College of Information Engineering, Taizhou University, Taizhou, Jiangsu, China
| | - Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong, China
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Sun J, Xu M, Ru J, James-Bott A, Xiong D, Wang X, Cribbs AP. Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications. Eur J Med Chem 2023; 257:115500. [PMID: 37262996 DOI: 10.1016/j.ejmech.2023.115500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 06/03/2023]
Abstract
Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.
Collapse
Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Miaoer Xu
- Department of Biology, Emory University, Atlanta, GA, 30322, USA
| | - Jinlong Ru
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, 85354, Germany
| | - Anna James-Bott
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Xia Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
| | - Adam P Cribbs
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| |
Collapse
|
5
|
Sun J, Ru J, Ramos-Mucci L, Qi F, Chen Z, Chen S, Cribbs AP, Deng L, Wang X. DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning. Int J Mol Sci 2023; 24:1878. [PMID: 36768205 PMCID: PMC9915273 DOI: 10.3390/ijms24031878] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/26/2022] [Accepted: 01/12/2023] [Indexed: 01/21/2023] Open
Abstract
Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.
Collapse
Affiliation(s)
- Jianfeng Sun
- College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Jinlong Ru
- Institute of Virology, Helmholtz Centre Munich—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Lorenzo Ramos-Mucci
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Fei Qi
- Institute of Genomics, School of Medicine, Huaqiao University, Xiamen 362021, China
| | - Zihao Chen
- Department of Computational Biology for Drug Discovery, Biolife Biotechnology Ltd., Zhumadian 463200, China
| | - Suyuan Chen
- Leibniz-Institut für Analytische Wissenschaften–ISAS–e.V., Otto-Hahn-Str asse 6b, 44227 Dortmund, Germany
| | - Adam P. Cribbs
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Li Deng
- Institute of Virology, Helmholtz Centre Munich—German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Xia Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA
| |
Collapse
|
6
|
MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations. BIOLOGY 2022; 12:biology12010041. [PMID: 36671734 PMCID: PMC9855084 DOI: 10.3390/biology12010041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022]
Abstract
Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug-miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the associations between drugs and miRNAs through wet experiments is time-consuming and laborious. Therefore, it is significant to develop computational prediction methods to improve the efficiency of identifying DMA on a large scale. In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug-miRNA association. Specifically, we first formulated known DMA as a bipartite graph and utilized structural deep network embedding (SDNE) to learn the topological features from the graph. Second, the Word2vec algorithm was utilized to construct the attribute features of the miRNAs and drugs. Third, two kinds of features were entered into the convolution neural network (CNN) and deep neural network (DNN) to integrate features and predict potential target miRNAs for the drugs. To evaluate the MFIDMA model, it was implemented on three different datasets under a five-fold cross-validation and achieved average AUCs of 0.9407, 0.9444 and 0.8919. In addition, the MFIDMA model showed reliable results in the case studies of Verapamil and hsa-let-7c-5p, confirming that the proposed model can also predict DMA in real-world situations. The model was effective in analyzing the neighbors and topological features of the drug-miRNA network by SDNE. The experimental results indicated that the MFIDMA is an accurate and robust model for predicting potential DMA, which is significant for miRNA therapeutics research and drug discovery.
Collapse
|
7
|
Wu T, Gao YY, Tang XN, Li Y, Dai J, Zhou S, Wu M, Zhang JJ, Wang SX. Construction of a competing endogenous RNA network to identify drug targets against polycystic ovary syndrome. Hum Reprod 2022; 37:2856-2866. [PMID: 36223608 DOI: 10.1093/humrep/deac218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 08/01/2022] [Indexed: 12/14/2022] Open
Abstract
STUDY QUESTION Would the construction of a competing endogenous RNA (ceRNA) network help identify new drug targets for the development of potential therapies for polycystic ovary syndrome (PCOS)? SUMMARY ANSWER Both Food and Drug Administartion (FDA)-approved and candidate drugs could be identified by combining bioinformatics approaches with clinical sample analysis based on our established ceRNA network. WHAT IS KNOWN ALREADY Thus far, no effective drugs are available for treating PCOS. ceRNAs play crucial roles in multiple diseases, and some of them are in current use as prognostic biomarkers as well as for chemo-response and drug prediction. STUDY DESIGN, SIZE, DURATION For the bioinformatics part, five microarrays of human granulosa cells were considered eligible after applying strict screening criteria and were used to construct the ceRNA network for target identification. For population-based validation, samples from 24 women with and without PCOS were collected from January 2021 to July 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS The public data included 27 unaffected women and 25 women with PCOS, according to the Rotterdam criteria proposed in 2003. The limma and RobustRankAggreg R packages were used to identify differentially expressed messenger RNAs and noncoding RNAs. Gene Ontology, Reactome and Kyoto Encyclopedia of Genes and Gemomes (KEGG) enrichment analyses were performed. A ceRNA network was constructed by integrating the differentially expressed genes and target genes. The population-based validation included human luteinized granulosa cell samples from 12 unaffected women and 12 women with PCOS. Quantitative real-time polymerase chain reaction was conducted to detect the levels of mRNAs and microRNAs (miRNAs). Connectivity map and computational model algorithms were implemented to predict therapeutic drugs from the ceRNA network. Additionally, we compared the predicted drugs with known clinical medications in DrugBank. MAIN RESULTS AND THE ROLE OF CHANCE A set of 10 mRNAs, 11 miRNAs and 53 long non-coding RNAs (lncRNAs) were differentially expressed. Functional enrichment analysis revealed the highest relevance to immune system-related biological processes and signalling pathways, such as cytokine secretion and leucocyte chemotaxis. A ceRNA consisting of two lncRNAs, two miRNAs and five mRNAs was constructed. Through network construction via bioinformatic analysis, we identified some already approved drugs (such as metformin) that could target some molecules in the network as potential drug candidates for PCOS. LARGE SCALE DATA Public sequencing data were obtained from GSE34526, GSE84376, GSE102293, GSE106724 and GSE114419, which have been deposited in the Gene Expression Omnibus database. LIMITATIONS, REASONS FOR CAUTION Experiments, such as immunoprecipitation, luciferase reporter assays and animal model studies, are needed to validate the potential targets in the ceRNA network before the identified drug candidates can be tested using cellular and animal model systems. WIDER IMPLICATIONS OF THE FINDINGS Our findings provide new bioinformatic insight into the possible pathogenesis of PCOS from ceRNA network analysis, which has not been previously studied in the human reproductive field. Our study also reveals some potential drug candidates for the future development of possible therapies against PCOS. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by grants from the National Key Research and Development Program of China (2021YFC2700400) and the National Natural Science Foundation of China (82001498). The authors have no conflicts of interest to disclose.
Collapse
Affiliation(s)
- Tong Wu
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yue-Yue Gao
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xia-Nan Tang
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Li
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Dai
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Su Zhou
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Meng Wu
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jin-Jin Zhang
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shi-Xuan Wang
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
8
|
Ni J, Cheng X, Ni T, Liang J. Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition. Front Mol Biosci 2022; 9:1009099. [PMID: 36504714 PMCID: PMC9732030 DOI: 10.3389/fmolb.2022.1009099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/03/2022] [Indexed: 11/27/2022] Open
Abstract
The accurate prediction of potential associations between microRNAs (miRNAs) and small molecule (SM) drugs can enhance our knowledge of how SM cures endogenous miRNA-related diseases. Given that traditional methods for predicting SM-miRNA associations are time-consuming and arduous, a number of computational models have been proposed to anticipate the potential SM-miRNA associations. However, several of these strategies failed to eliminate noise from the known SM-miRNA association information or failed to prioritize the most significant known SM-miRNA associations. Therefore, we proposed a model of Graph Convolutional Network with Layer Attention mechanism for SM-MiRNA Association prediction (GCNLASMMA). Firstly, we obtained the new SM-miRNA associations by matrix decomposition. The new SM-miRNA associations, as well as the integrated SM similarity and miRNA similarity were subsequently incorporated into a heterogeneous network. Finally, a graph convolutional network with an attention mechanism was used to compute the reconstructed SM-miRNA association matrix. Furthermore, four types of cross validations and two types of case studies were performed to assess the performance of GCNLASMMA. In cross validation, global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation achieved excellent performance. Numerous hypothesized associations in case studies were confirmed by experimental literatures. All of these results confirmed that GCNLASMMA is a trustworthy association inference method.
Collapse
|
9
|
Peng L, Tu Y, Huang L, Li Y, Fu X, Chen X. DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder. Brief Bioinform 2022; 23:6827720. [PMID: 36377749 DOI: 10.1093/bib/bbac478] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 09/28/2022] [Accepted: 10/08/2022] [Indexed: 11/16/2022] Open
Abstract
MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating gene expression, but also having an important role in human life activities and being viable targets of small molecule drugs for disease treatment. Current computational techniques to predict the potential associations between small molecule and miRNA are not that accurate. Here, we proposed a new computational method based on a deep autoencoder and a scalable tree boosting model (DAESTB), to predict associations between small molecule and miRNA. First, we constructed a high-dimensional feature matrix by integrating small molecule-small molecule similarity, miRNA-miRNA similarity and known small molecule-miRNA associations. Second, we reduced feature dimensionality on the integrated matrix using a deep autoencoder to obtain the potential feature representation of each small molecule-miRNA pair. Finally, a scalable tree boosting model is used to predict small molecule and miRNA potential associations. The experiments on two datasets demonstrated the superiority of DAESTB over various state-of-the-art methods. DAESTB achieved the best AUC value. Furthermore, in three case studies, a large number of predicted associations by DAESTB are confirmed with the public accessed literature. We envision that DAESTB could serve as a useful biological model for predicting potential small molecule-miRNA associations.
Collapse
Affiliation(s)
- Li Peng
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China.,Hunan Key Laboratory for Service computing and Novel Software Technology
| | - Yuan Tu
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Yang Li
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Xiangzheng Fu
- College of Information Science and Engineering, Hunan University, Changsha, 410082, Hunan, China
| | - Xiang Chen
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China
| |
Collapse
|
10
|
Duan T, Kuang Z, Deng L. SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network. Front Oncol 2022; 12:987609. [PMID: 36338674 PMCID: PMC9632662 DOI: 10.3389/fonc.2022.987609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/14/2022] [Indexed: 11/21/2022] Open
Abstract
In recent years, the miRNA is considered as a potential high-value therapeutic target because of its complex and delicate mechanism of gene regulation. The abnormal expression of miRNA can cause drug resistance, affecting the therapeutic effect of the disease. Revealing the associations between miRNAs-drug resistance can help in the design of effective drugs or possible drug combinations. However, current conventional experiments for identification of miRNAs-drug resistance are time-consuming and high-cost. Therefore, it’s of pretty realistic value to develop an accurate and efficient computational method to predicting miRNAs-drug resistance. In this paper, a method based on the Support Vector Machines (SVM) to predict the association between MiRNA and Drug Resistance (SVMMDR) is proposed. The SVMMDR integrates miRNAs-drug resistance association, miRNAs sequence similarity, drug chemical structure similarity and other similarities, extracts path-based Hetesim features, and obtains inclined diffusion feature through restart random walk. By combining the multiple feature, the prediction score between miRNAs and drug resistance is obtained based on the SVM. The innovation of the SVMMDR is that the inclined diffusion feature is obtained by inclined restart random walk, the node information and path information in heterogeneous network are integrated, and the SVM is used to predict potential miRNAs-drug resistance associations. The average AUC of SVMMDR obtained is 0.978 in 10-fold cross-validation.
Collapse
|
11
|
A message passing framework with multiple data integration for miRNA-disease association prediction. Sci Rep 2022; 12:16259. [PMID: 36171337 PMCID: PMC9519928 DOI: 10.1038/s41598-022-20529-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 09/14/2022] [Indexed: 11/08/2022] Open
Abstract
Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach's superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.
Collapse
|
12
|
Ražná K, Harenčár Ľ, Kučka M. The Involvement of microRNAs in Plant Lignan Biosynthesis—Current View. Cells 2022; 11:cells11142151. [PMID: 35883592 PMCID: PMC9323225 DOI: 10.3390/cells11142151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 02/01/2023] Open
Abstract
Lignans, as secondary metabolites synthesized within a phenylpropanoid pathway, play various roles in plants, including their involvement in growth and plant defense processes. The health and nutritional benefits of lignans are unquestionable, and many studies have been devoted to these attributes. Although the regulatory role of miRNAs in the biosynthesis of secondary metabolites has been widely reported, there is no systematic review available on the miRNA-based regulatory mechanism of lignans biosynthesis. However, the genetic background of lignan biosynthesis in plants is well characterized. We attempted to put together a regulatory mosaic based on current knowledge describing miRNA-mediated regulation of genes, enzymes, or transcription factors involved in this biosynthesis process. At the same time, we would like to underline the fact that further research is necessary to improve our understanding of the miRNAs regulating plant lignan biosynthesis by exploitation of current approaches for functional identification of miRNAs.
Collapse
|
13
|
Rashidi S, Mansouri R, Ali-Hassanzadeh M, Ghani E, Karimazar M, Muro A, Nguewa P, Manzano-Román R. miRNAs in the regulation of mTOR signaling and host immune responses: The case of Leishmania infections. Acta Trop 2022; 231:106431. [PMID: 35367408 DOI: 10.1016/j.actatropica.2022.106431] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/23/2022] [Accepted: 03/26/2022] [Indexed: 11/01/2022]
Abstract
Micro RNAs (miRNAs), as regulators of gene expression at the post-transcriptional level, can respond to/or interact with cell signaling and affect the pathogenesis of different diseases/infections. The interaction/crosstalk of miRNAs with various cellular signaling networks including mTOR (as a master regulator of signaling relevant to different cellular mechanisms) might lead to the initiation, progression or restriction of certain disease processes. There are numerous studies that have identified the crosstalk between regulatory miRNA expression and the mTOR pathway (or mTOR signaling regulated by miRNAs) in different diseases which has a dual function in pathogenesis. However, the corresponding information in parasitic infections remains scarce. miRNAs have been suggested as specific targets for therapeutic strategies in several disorders such as parasitic infections. Thus, the targeting of miRNAs (as the modulators/regulators of mTOR) by small molecules and RNA-based therapeutics and consequently managing and modulating mTOR signaling and the downstream/related cell signaling/pathways might shed some light on the design of new therapeutic strategies against parasitic diseases, including Leishmaniasis. Accordingly, the present study attempts to highlight the importance of the crosstalk between regulatory miRNAs and mTOR signaling, and to review the relevant insights into parasitic infections by focusing specifically on Leishmania.
Collapse
|
14
|
Zheng J, Xiao X, Qiu WR. DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method. Front Genet 2022; 13:859188. [PMID: 35754843 PMCID: PMC9213727 DOI: 10.3389/fgene.2022.859188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/25/2022] [Indexed: 11/20/2022] Open
Abstract
Drug–target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.
Collapse
Affiliation(s)
- Jie Zheng
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Xuan Xiao
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| | - Wang-Ren Qiu
- Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China
| |
Collapse
|
15
|
Aldahdooh J, Vähä-Koskela M, Tang J, Tanoli Z. Using BERT to identify drug-target interactions from whole PubMed. BMC Bioinformatics 2022; 23:245. [PMID: 35729494 PMCID: PMC9214985 DOI: 10.1186/s12859-022-04768-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 06/03/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs. To this end, we applied Bidirectional Encoder Representations from Transformers (BERT) to identify such articles. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format. RESULTS Our novel method identified 0.6 million articles (along with drug and protein information) which are not previously included in public DTI databases. Using 10-fold cross-validation, we obtained ~ 99% accuracy for identifying articles containing quantitative drug-target profiles. The F1 micro for the prediction of assay format is 88%, which leaves room for improvement in future studies. CONCLUSION The BERT model in this study is robust and the proposed pipeline can be used to identify previously overlooked articles containing quantitative DTIs. Overall, our method provides a significant advancement in machine-assisted DTI extraction and curation. We expect it to be a useful addition to drug mechanism discovery and repurposing.
Collapse
Affiliation(s)
- Jehad Aldahdooh
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Doctoral Programme in Computer Science, University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Ziaurrehman Tanoli
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. .,BioICAWtech, Helsinki, Finland.
| |
Collapse
|
16
|
Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0. BIG DATA AND COGNITIVE COMPUTING 2022; 6. [PMID: 35936510 PMCID: PMC9351549 DOI: 10.3390/bdcc6010027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.
Collapse
|
17
|
Sun C, Xuan P, Zhang T, Ye Y. Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:455-464. [PMID: 32750854 DOI: 10.1109/tcbb.2020.2999084] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by constructing shallow prediction models. These methods failed to deeply learn the low-dimension feature vectors for drugs and targets and ignored the distribution of these feature vectors. We proposed a graph convolutional autoencoder and generative adversarial network (GAN)-based method, GANDTI, to predict DTIs. We constructed a drug-target heterogeneous network to integrate various connections related to drugs and targets, i.e., the similarities and interactions between drugs or between targets and the interactions between drugs and targets. A graph convolutional autoencoder was established to learn the network embeddings of the drug and target nodes in a low-dimensional feature space, and the autoencoder deeply integrated different kinds of connections within the network. A GAN was introduced to regularize the feature vectors of nodes into a Gaussian distribution. Severe class imbalance exists between known and unknown DTIs. Thus, we constructed a classifier based on an ensemble learning model, LightGBM, to estimate the interaction propensities of drugs and targets. This classifier completely exploited all unknown DTIs and counteracted the negative effect of class imbalance. The experimental results indicated that GANDTI outperforms several state-of-the-art methods for DTI prediction. Additionally, case studies of five drugs demonstrated the ability of GANDTI to discover the potential targets for drugs.
Collapse
|
18
|
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.
Collapse
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.)
| |
Collapse
|
19
|
Wang CC, Han CD, Zhao Q, Chen X. Circular RNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2021; 22:bbab286. [PMID: 34329377 PMCID: PMC8575014 DOI: 10.1093/bib/bbab286] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/23/2021] [Accepted: 07/03/2021] [Indexed: 12/13/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.
Collapse
Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology
| | - Chen-Di Han
- School of Information and Control Engineering, China University of Mining and Technology
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning
| | - Xing Chen
- China University of Mining and Technology
| |
Collapse
|
20
|
Luo J, Shen C, Lai Z, Cai J, Ding P. Incorporating Clinical, Chemical and Biological Information for Predicting Small Molecule-microRNA Associations Based on Non-Negative Matrix Factorization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2535-2545. [PMID: 32092012 DOI: 10.1109/tcbb.2020.2975780] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Small molecule(SM) drugs can affect the expression of miRNAs, which plays crucial roles in many important biological processes. The chemical structure and clinical information of small molecule can simultaneously incorporate information such as anatomical distribution, therapeutic effects and structural characteristics. It is necessary to develop a novel model that incorporates small molecule chemical structure and clinical information to reveal the unknown small molecule-miRNA associations. In this study, we developed a new framework based on non-negative matrix factorization, called SMANMF, to discover the potential small molecules-miRNAs associations. First, the functional similarity of two miRNAs can be obtained by computing the overlap of the target gene sets in which the miRNAs interact together, and we integrated two types of small molecule similarities, including chemical similarity and clinical similarity. Then, we utilized a non-negative matrix factorization model to discover the unknown relationship between small molecules and miRNAs. The evaluation results indicate that our model can achieve superior prediction performance compared with previous approaches in 5-fold cross-validation. At the same time, the results of case studies also reveal that the SMANMF model has good predictive performance for predicting the potential association between small molecules and miRNAs.
Collapse
|
21
|
Ding P, Ouyang W, Luo J, Kwoh CK. Heterogeneous information network and its application to human health and disease. Brief Bioinform 2021; 21:1327-1346. [PMID: 31566212 DOI: 10.1093/bib/bbz091] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/29/2019] [Accepted: 06/30/2019] [Indexed: 12/11/2022] Open
Abstract
The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.
Collapse
Affiliation(s)
- Pingjian Ding
- School of Computer Science, University of South China, Hengyang, China
| | - Wenjue Ouyang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Chee-Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| |
Collapse
|
22
|
Ding P, Liang C, Ouyang W, Li G, Xiao Q, Luo J. Inferring Synergistic Drug Combinations Based on Symmetric Meta-Path in a Novel Heterogeneous Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1562-1571. [PMID: 31714232 DOI: 10.1109/tcbb.2019.2951557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Combinatorial drug therapy is a promising way for treating cancers, which can reduce drug side effects and improve drug efficacy. However, due to the large-scale combinatorial space, it is difficult to quickly and effectively identify novel synergistic drug combinations for further implementing combinatorial drug therapy. The computational method of fusing multi-source knowledge is a time- and cost-efficient strategy to infer synergistic drug combinations for testing. However, for the existing computational methods of inferring synergistic drug combinations, it still remains a challenging to effectively combine multi-source information to achieve the desired results. Hence, in this study, we developed a novel Inference method of Synergistic Drug Combinations based on Symmetric Meta-Path (ISDCSMP), which can systematically and accurately prioritize synergistic drug combinations in a novel drug-target heterogeneous network integrating multi-source information. In the experiment, ISDCSMP outperformed the state-of-the-art methods in terms of AUC and precision on the benchmark dataset in five-fold cross validation. Moreover, we further illustrated performances of different ways for obtaining the combination coefficients, and analyzed the influences of the maximum meta-path length. The performances of various single meta-paths were described in five-fold cross validation. Finally, we confirmed the practical usefulness of ISDCSMP with the predicted novel synergistic drug combinations. The source code of ISDCSMP is available at https://github.com/KDDing/ISDCSMP.
Collapse
|
23
|
Pliakos K, Vens C, Tsoumakas G. Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1596-1607. [PMID: 31689203 DOI: 10.1109/tcbb.2019.2951378] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Identifying drug-target interactions is crucial for drug discovery. Despite modern technologies used in drug screening, experimental identification of drug-target interactions is an extremely demanding task. Predicting drug-target interactions in silico can thereby facilitate drug discovery as well as drug repositioning. Various machine learning models have been developed over the years to predict such interactions. Multi-output learning models in particular have drawn the attention of the scientific community due to their high predictive performance and computational efficiency. These models are based on the assumption that all the labels are correlated with each other. However, this assumption is too optimistic. Here, we address drug-target interaction prediction as a multi-label classification task that is combined with label partitioning. We show that building multi-output learning models over groups (clusters) of labels often leads to superior results. The performed experiments confirm the efficiency of the proposed framework.
Collapse
|
24
|
Li J, Peng D, Xie Y, Dai Z, Zou X, Li Z. Novel Potential Small Molecule-MiRNA-Cancer Associations Prediction Model Based on Fingerprint, Sequence, and Clinical Symptoms. J Chem Inf Model 2021; 61:2208-2219. [PMID: 33899462 DOI: 10.1021/acs.jcim.0c01458] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
As an important biomarker in organisms, miRNA is closely related to various small molecules and diseases. Research on small molecule-miRNA-cancer associations is helpful for the development of cancer treatment drugs and the discovery of pathogenesis. It is very urgent to develop theoretical methods for identifying potential small molecular-miRNA-cancer associations, because experimental approaches are usually time-consuming, laborious, and expensive. To overcome this problem, we developed a new computational method, in which features derived from structure, sequence, and symptoms were utilized to characterize small molecule, miRNA, and cancer, respectively. A feature vector was construct to characterize small molecule-miRNA-cancer association by concatenating these features, and a random forest algorithm was utilized to construct a model for recognizing potential association. Based on the 5-fold cross-validation and benchmark data set, the model achieved an accuracy of 93.20 ± 0.52%, a precision of 93.22 ± 0.51%, a recall of 93.20 ± 0.53%, and an F1-measure of 93.20 ± 0.52%. The areas under the receiver operating characteristic curve and precision recall curve were 0.9873 and 0.9870. The real prediction ability and application performance of the developed method have also been further evaluated and verified through an independent data set test and case study. Some potential small molecules and miRNAs related to cancer have been identified and are worthy of further experimental research. It is anticipated that our model could be regarded as a useful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Multi-class-SMMCA.
Collapse
Affiliation(s)
- Jinlong Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China
| | - Dongdong Peng
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China
| | - Yun Xie
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China
| | - Zong Dai
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Xiaoyong Zou
- School of Chemistry, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Zhanchao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China
- Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of State Administration of Traditional Chinese Medicine, Guangzhou 510006, People's Republic of China
| |
Collapse
|
25
|
Jamali AA, Kusalik A, Wu FX. MDIPA: a microRNA-drug interaction prediction approach based on non-negative matrix factorization. Bioinformatics 2021; 36:5061-5067. [PMID: 33212495 DOI: 10.1093/bioinformatics/btaa577] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/27/2020] [Accepted: 06/11/2020] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA-drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA-drug interactions. RESULTS In this study, a matrix factorization-based method, called the microRNA-drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA-drug interactions. AVAILABILITY AND IMPLEMENTATION All code and data are freely available from https://github.com/AliJam82/MDIPA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | - Anthony Kusalik
- Division of Biomedical Engineering.,Department of Computer Science
| | - Fang-Xiang Wu
- Division of Biomedical Engineering.,Department of Computer Science.,Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| |
Collapse
|
26
|
Clinico-Pathological Importance of miR-146a in Lung Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020274. [PMID: 33578944 PMCID: PMC7916675 DOI: 10.3390/diagnostics11020274] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/02/2021] [Accepted: 02/05/2021] [Indexed: 12/12/2022] Open
Abstract
Lung cancer is a well-known malignant tumor of the respiratory tract, which has caused a significant level of damage to human health in the 21st century. Micro-RNAs (miRNAs) are tiny, non-coding RNA stem-loop structures with a length of roughly 20–25 nucleotides that function as powerful modulators of mRNA and protein products of a gene. miRNAs may modulate many biological processes involving growth, differentiation, proliferation, and cell death and play a key role in the pathogenesis of various types of malignancies. Several accumulating pieces of evidence have proven that miRNA, especially miR-146a, are crucial modulators of innate immune response sequences. A novel and exciting cancer research field has involved miRNA for the detection and suppression of cancer. However, the actual mechanism which is adopted by these miRNA is still unclear. miRNAs have been used as a cancer-associated biomarker in several studies, suggesting their altered expression in various cancers compared to the normal cells. The amount of expression of miRNA can also be used to determine the stage of the disease, aiding in early detection. In breast, pancreatic, and hepatocellular carcinoma, and gastric cancer, cancer cell proliferation and metastasis has been suppressed by miR-146a. Changes in miR-146a expression levels have biomarker importance and possess a high potential as a therapeutic target in lung cancer. It retards epithelial-mesenchymal transition and promotes the therapeutic action of anticancer agents in lung cancer. Studies have also suggested that miR-146a affects gene expression through different signaling pathways viz. TNF-α, NF-κB and MEK-1/2, and JNK-1/2. Further research is required for understanding the molecular mechanisms of miR-146a in lung cancer. The potential role of miR-146a as a diagnostic marker of lung cancer must also be analyzed. This review summarizes the tumor-suppressing, anti-inflammatory, and antichemoresistive nature of miR-146a in lung cancer.
Collapse
|
27
|
Issa NT, Stathias V, Schürer S, Dakshanamurthy S. Machine and deep learning approaches for cancer drug repurposing. Semin Cancer Biol 2021; 68:132-142. [PMID: 31904426 PMCID: PMC7723306 DOI: 10.1016/j.semcancer.2019.12.011] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/31/2019] [Accepted: 12/15/2019] [Indexed: 02/07/2023]
Abstract
Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.
Collapse
Affiliation(s)
- Naiem T Issa
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami School of Medicine, Miami, FL, USA
| | - Vasileios Stathias
- Department of Molecular and Cellular Pharmacology, University of Miami School of Medicine, Miami, FL, USA
| | - Stephan Schürer
- Department of Molecular and Cellular Pharmacology, University of Miami School of Medicine, Miami, FL, USA
| | - Sivanesan Dakshanamurthy
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.
| |
Collapse
|
28
|
The Expression Patterns of BECN1, LAMP2, and PINK1 Genes in Colorectal Cancer Are Potentially Regulated by Micrornas and CpG Islands: An In Silico Study. J Clin Med 2020; 9:jcm9124020. [PMID: 33322704 PMCID: PMC7764710 DOI: 10.3390/jcm9124020] [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: 11/03/2020] [Revised: 12/06/2020] [Accepted: 12/10/2020] [Indexed: 12/15/2022] Open
Abstract
Background: Autophagy plays a dual role of tumor suppression and tumor promotion in colorectal cancer. The study aimed to find those microRNAs (miRNAs) important in BECN1, LAMP2, and PINK1 regulation and to determine the possible role of the epigenetic changes in examined colorectal cancer using an in silico approach. Methods: A total of 44 pairs of surgically removed tumors at clinical stages I‒IV and healthy samples (marginal tissues) from patients’ guts were analyzed. Analysis of the obtained results was conducted using the PL-Grid Infrastructure and Statistica 12.0 program. The miRNAs and CpG islands were estimated using the microrna.org database and MethPrimer program. Results: The autophagy-related genes were shown to be able to be regulated by miRNAs (BECN1—49 mRNA, LAMP2—62 mRNA, PINK1—6 mRNA). It was observed that promotion regions containing at least one CpG region were present in the sequence of each gene. Conclusions: The in silico analysis performed allowed us to determine the possible role of epigenetic mechanisms of regulation gene expression, which may be an interesting therapeutic target in the treatment of colorectal cancer.
Collapse
|
29
|
Shen C, Luo J, Ouyang W, Ding P, Wu H. Identification of Small Molecule–miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks. J Chem Inf Model 2020; 60:6709-6721. [DOI: 10.1021/acs.jcim.0c00975] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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
| | - Wenjue Ouyang
- 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
| | - Hao Wu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| |
Collapse
|
30
|
Lee LK, Medzikovic L, Eghbali M, Eltzschig HK, Yuan X. The Role of MicroRNAs in Acute Respiratory Distress Syndrome and Sepsis, From Targets to Therapies: A Narrative Review. Anesth Analg 2020; 131:1471-1484. [PMID: 33079870 PMCID: PMC8532045 DOI: 10.1213/ane.0000000000005146] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Acute respiratory distress syndrome (ARDS) is a significant cause of morbidity and mortality in the intensive care unit (ICU) and is characterized by lung epithelial and endothelial cell injury, with increased permeability of the alveolar-capillary membrane, leading to pulmonary edema, severe hypoxia, and difficulty with ventilation. The most common cause of ARDS is sepsis, and currently, treatment of ARDS and sepsis has consisted mostly of supportive care because targeted therapies have largely been unsuccessful. The molecular mechanisms behind ARDS remain elusive. Recently, a number of microRNAs (miRNAs) identified through high-throughput screening studies in ARDS patients and preclinical animal models have suggested a role for miRNA in the pathophysiology of ARDS. miRNAs are small noncoding RNAs ranging from 18 to 24 nucleotides that regulate gene expression via inhibition of the target mRNA translation or by targeting complementary mRNA for early degradation. Unsurprisingly, some miRNAs that are differentially expressed in ARDS overlap with those important in sepsis. In addition, circulatory miRNA may be useful as biomarkers or as targets for pharmacologic therapy. This can be revolutionary in a syndrome that has neither a measurable indicator of the disease nor a targeted therapy. While there are currently no miRNA-based therapies targeted for ARDS, therapies targeting miRNA have reached phase II clinical trials for the treatment of a wide range of diseases. Further studies may yield a unique miRNA profile pattern that serves as a biomarker or as targets for miRNA-based pharmacologic therapy. In this review, we discuss miRNAs that have been found to play a role in ARDS and sepsis, the potential mechanism of how particular miRNAs may contribute to the pathophysiology of ARDS, and strategies for pharmacologically targeting miRNA as therapy.
Collapse
Affiliation(s)
- Lisa K. Lee
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California
| | - Lejla Medzikovic
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California
| | - Mansoureh Eghbali
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California
| | - Holger K. Eltzschig
- Department of Anesthesiology, The University of Texas Health Science Center, McGovern Medical School, Houston, Texas
| | - Xiaoyi Yuan
- Department of Anesthesiology, The University of Texas Health Science Center, McGovern Medical School, Houston, Texas
| |
Collapse
|
31
|
Ma P, Yue L, Zhang S, Hao D, Wu Z, Xu L, Du G, Xiao P. Target RNA modification for epigenetic drug repositioning in neuroblastoma: computational omics proximity between repurposing drug and disease. Aging (Albany NY) 2020; 12:19022-19044. [PMID: 33044945 PMCID: PMC7732279 DOI: 10.18632/aging.103671] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 06/29/2020] [Indexed: 01/24/2023]
Abstract
RNA modifications modulate most steps of gene expression. However, little is known about its role in neuroblastoma (NBL) and the inhibitors targeting it. We analyzed the RNA-seq (n=122) and CNV data (n=78) from NBL patients in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The NBL sub-clusters (cluster1/2) were identified via consensus clustering for expression of RNA modification regulators (RNA-MRs). Cox regression, principle component analysis and chi-square analysis were used to compare differences of survival, transcriptome, and clinicopathology between clusters. Cluster1 showed significantly poor prognosis, of which RNA-MRs' expression and CNV alteration were closely related to pathologic stage. RNA-MRs and functional related prognostic genes were obtained using spearman correlation analysis, and queried in CMap and L1000 FWD database to obtain 88 inhibitors. The effects of 5 inhibitors on RNA-MRs were confirmed in SH-SY5Y cells. The RNA-MRs exhibited two complementary regulation functions: one conducted by TET2 and related to translation and glycolysis; another conducted by ALYREF, NSUN2 and ADARB1 and related to cell cycle and DNA repair. The perturbed proteomic profile of HDAC inhibitors was different from that of others, thus drug combination overcame drug resistance and was potential for NBL therapy with RNA-MRs as therapeutic targets.
Collapse
Affiliation(s)
- Pei Ma
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China,State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Lifeng Yue
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Sen Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Dacheng Hao
- Biotechnology Institute, School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116021, China
| | - Zhihong Wu
- Department of Orthopedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Lijia Xu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China
| | - Guanhua Du
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Peigen Xiao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China
| |
Collapse
|
32
|
Zou H, Li Y, Liu X, Wu Z, Li J, Ma Z. Roles of plant-derived bioactive compounds and related microRNAs in cancer therapy. Phytother Res 2020; 35:1176-1186. [PMID: 33000538 DOI: 10.1002/ptr.6883] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/03/2020] [Accepted: 08/31/2020] [Indexed: 12/17/2022]
Abstract
Plant-derived bioactive compounds, often called phytochemicals, are active substances extracted from different plants. These bioactive compounds can release therapeutic potential abilities via reducing antitumor drugs side effects or directly killing cancer cells, and others also can adjust cancer initiation and progression via regulating microRNAs (miRNAs) expression, and miRNA can regulate protein-coding expression by restraining translation or degrading target mRNA. A mass of research showed that plant-derived bioactive compounds including tanshinones, astragaloside IV, berberine, ginsenosides and matrine can inhibit tumor growth and metastasis by rescuing aberrant miRNAs expression, which has influence on tumor progression, microenvironment and drug resistance in multifarious cancers. This review aims to provide a novel understanding of plant-derived bioactive compounds targeting miRNAs and shed light on their future clinical applications.
Collapse
Affiliation(s)
- Heng Zou
- Lab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| | - Yanli Li
- Lab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| | - Xiaomin Liu
- Lab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| | - Zong Wu
- Lab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| | - Jingjing Li
- School of Pharmaceutical Engineering, Zhejiang Pharmaceutical College, Ningbo, China
| | - Zhongliang Ma
- Lab for Noncoding RNA & Cancer, School of Life Sciences, Shanghai University, Shanghai, China
| |
Collapse
|
33
|
Zhao Y, Wang CC, Chen X. Microbes and complex diseases: from experimental results to computational models. Brief Bioinform 2020; 22:5882184. [PMID: 32766753 DOI: 10.1093/bib/bbaa158] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 12/13/2022] Open
Abstract
Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastrointestinal tract and strengthening metabolic function. In addition, in recent years, more and more research results have indicated that there are close relationships between the emergence of the human noncommunicable diseases and microbes, which provides a novel insight for us to further understand the pathogenesis of the diseases. An in-depth study about the relationships between diseases and microbes will not only contribute to exploring new strategies for the diagnosis and treatment of diseases but also significantly heighten the efficiency of new drugs development. However, applying the methods of biological experimentation to reveal the microbe-disease associations is costly and inefficient. In recent years, more and more researchers have constructed multiple computational models to predict microbes that are potentially associated with diseases. Here, we start with a brief introduction of microbes and databases as well as web servers related to them. Then, we mainly introduce four kinds of computational models, including score function-based models, network algorithm-based models, machine learning-based models and experimental analysis-based models. Finally, we summarize the advantages as well as disadvantages of them and set the direction for the future work of revealing microbe-disease associations based on computational models. We firmly believe that computational models are expected to be important tools in large-scale predictions of disease-related microbes.
Collapse
Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining
| |
Collapse
|
34
|
Chen H, Cheng F, Li J. iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding. PLoS Comput Biol 2020; 16:e1008040. [PMID: 32667925 PMCID: PMC7384678 DOI: 10.1371/journal.pcbi.1008040] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 07/27/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022] Open
Abstract
Computational drug repositioning and drug-target prediction have become essential tasks in the early stage of drug discovery. In previous studies, these two tasks have often been considered separately. However, the entities studied in these two tasks (i.e., drugs, targets, and diseases) are inherently related. On one hand, drugs interact with targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases. On the other hand, both drug repositioning and drug-target prediction involve the same drug feature space, which naturally connects these two problems and the two domains (diseases and targets). By using the wisdom of the crowds, it is possible to transfer knowledge from one of the domains to the other. The existence of relationships among drug-target-disease motivates us to jointly consider drug repositioning and drug-target prediction in drug discovery. In this paper, we present a novel approach called iDrug, which seamlessly integrates drug repositioning and drug-target prediction into one coherent model via cross-network embedding. In particular, we provide a principled way to transfer knowledge from these two domains and to enhance prediction performance for both tasks. Using real-world datasets, we demonstrate that iDrug achieves superior performance on both learning tasks compared to several state-of-the-art approaches. Our code and datasets are available at: https://github.com/Case-esaC/iDrug.
Collapse
Affiliation(s)
- Huiyuan Chen
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Jing Li
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- * E-mail:
| |
Collapse
|
35
|
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
| |
Collapse
|
36
|
Wang CC, Zhao Y, Chen X. Drug-pathway association prediction: from experimental results to computational models. Brief Bioinform 2020; 22:5835554. [PMID: 32393976 DOI: 10.1093/bib/bbaa061] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/16/2020] [Accepted: 03/26/2020] [Indexed: 12/14/2022] Open
Abstract
Effective drugs are urgently needed to overcome human complex diseases. However, the research and development of novel drug would take long time and cost much money. Traditional drug discovery follows the rule of one drug-one target, while some studies have demonstrated that drugs generally perform their task by affecting related pathway rather than targeting single target. Thus, the new strategy of drug discovery, namely pathway-based drug discovery, have been proposed. Obviously, identifying associations between drugs and pathways plays a key role in the development of pathway-based drug discovery. Revealing the drug-pathway associations by experiment methods would take much time and cost. Therefore, some computational models were established to predict potential drug-pathway associations. In this review, we first introduced the background of drug and the concept of drug-pathway associations. Then, some publicly accessible databases and web servers about drug-pathway associations were listed. Next, we summarized some state-of-the-art computational methods in the past years for inferring drug-pathway associations and divided these methods into three classes, namely Bayesian spare factor-based, matrix decomposition-based and other machine learning methods. In addition, we introduced several evaluation strategies to estimate the predictive performance of various computational models. In the end, we discussed the advantages and limitations of existing computational methods and provided some suggestions about the future directions of the data collection and the calculation models development.
Collapse
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
Ding P, Shen C, Lai Z, Liang C, Li G, Luo J. Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization. J Chem Inf Model 2020; 60:37-46. [PMID: 31891264 DOI: 10.1021/acs.jcim.9b00793] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations. Although many computational methods have focused on predicting drug synergy to reduce combinatorial space, they fail to effectively consider multiple sources of important knowledge. Thus, it is necessary to propose a computational method that can exploit useful information to predict drug synergy. Here, we developed a computational method to predict drug synergy based on graph co-regularization, named DSGCR. By incorporating drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations, DSGCR performs predictions of synergistic drug combinations. Compared to several existing methods, DSGCR achieves superior performance in predicting drug synergy in terms of various metrics via cross-validation. Additionally, we analyzed the importance of various sources of drug knowledge concerning three DSGCR's scenarios. Finally, the potential of DSGCR to score drug synergy was confirmed by three predicted synergistic drug combinations.
Collapse
Affiliation(s)
- Pingjian Ding
- School of Computer Science , University of South China , Hengyang 421001 , China
| | - Cong Shen
- College of Computer Science and Electronic Engineering , Hunan University , Changsha 410082 , China
| | - Zihan Lai
- College of Computer Science and Electronic Engineering , Hunan University , Changsha 410082 , China
| | - Cheng Liang
- School of Information Science and Engineering , Shandong Normal University , Jinan 250014 , China
| | - Guanghui Li
- School of Information Engineering , East China Jiaotong University , Nanchang 330013 , China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering , Hunan University , Changsha 410082 , China
| |
Collapse
|
39
|
Chu Y, Kaushik AC, Wang X, Wang W, Zhang Y, Shan X, Salahub DR, Xiong Y, Wei DQ. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform 2019; 22:451-462. [PMID: 31885041 DOI: 10.1093/bib/bbz152] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 12/18/2022] Open
Abstract
Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.
Collapse
Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | - Xiangeng Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Wei Wang
- Mathematical Sciences, Shanghai Jiao Tong University
| | - Yufang Zhang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | | | | | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University
| |
Collapse
|
40
|
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.
Collapse
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
| |
Collapse
|
41
|
Guan NN, Zhao Y, Wang CC, Li JQ, Chen X, Piao X. Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:164-174. [PMID: 31265947 PMCID: PMC6610642 DOI: 10.1016/j.omtn.2019.05.017] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/17/2019] [Accepted: 05/20/2019] [Indexed: 12/14/2022]
Abstract
Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.
Collapse
Affiliation(s)
- Na-Na Guan
- 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
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Xue Piao
- School of Medical Informatics, Xuzhou Medical University, Xuzhou 221004, China.
| |
Collapse
|