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Jurj A, Fontana B, Varani G, Calin GA. Small molecules targeting microRNAs: new opportunities and challenges in precision cancer therapy. Trends Cancer 2024:S2405-8033(24)00121-3. [PMID: 39107162 DOI: 10.1016/j.trecan.2024.06.006] [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: 11/27/2023] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 08/09/2024]
Abstract
Noncoding RNAs, especially miRNAs, play a pivotal role in cancer initiation and metastasis, underscoring their susceptibility to precise modulation via small molecule inhibitors. This review examines the innovative strategy of targeting oncogenic miRNAs with small drug-like molecules, an approach that can reshape the cancer treatment landscape. We review the current understanding of the multifaceted roles of miRNAs in oncogenesis, highlighting emerging therapeutic paradigms that have the potential to expand cancer treatment options. As research on small molecule inhibitors of miRNA is still in its early stages, ongoing investigative efforts and the development of new technologies and chemical matter are essential to fulfill the significant potential of this innovative approach to cancer treatment.
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Affiliation(s)
- Ancuta Jurj
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Beatrice Fontana
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Gabriele Varani
- Department of Chemistry, University of Washington, Seattle, WA 98195, USA.
| | - George A Calin
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Center for RNA Interference and Non-Coding RNAs, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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2
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Ding P, Zeng M, Yin R. Editorial: Computational methods to analyze RNA data for human diseases. Front Genet 2023; 14:1270334. [PMID: 37674479 PMCID: PMC10478215 DOI: 10.3389/fgene.2023.1270334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/14/2023] [Indexed: 09/08/2023] Open
Affiliation(s)
- Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Rui Yin
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
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3
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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.
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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.
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4
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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.
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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
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Luo Y, Peng L, Shan W, Sun M, Luo L, Liang W. Machine learning in the development of targeting microRNAs in human disease. Front Genet 2023; 13:1088189. [PMID: 36685965 PMCID: PMC9845262 DOI: 10.3389/fgene.2022.1088189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/12/2022] [Indexed: 01/05/2023] Open
Abstract
A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.
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Affiliation(s)
- Yuxun Luo
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China,Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China,Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China
| | - Wenyu Shan
- School of Computer Science, University of South China, Hengyang, China
| | - Mengyue Sun
- School of Polymer Science and Polymer Engineering, The University of Akron, Akron, OH, United States
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, China
| | - Wei Liang
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China,Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China,*Correspondence: Wei Liang,
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Li J, Lin H, Wang Y, Li Z, Wu B. Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning. Front Genet 2022; 13:1079053. [PMID: 36531225 PMCID: PMC9755196 DOI: 10.3389/fgene.2022.1079053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2023] Open
Abstract
MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.
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Affiliation(s)
- Jianwei Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
- Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China
| | - Hongxin Lin
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Yinfei Wang
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Zhiguang Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Baoqin Wu
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
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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.
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Zhong Y, Shen C, Wu H, Xu T, Luo L. Improving the Prediction of Potential Kinase Inhibitors with Feature Learning on Multisource Knowledge. Interdiscip Sci 2022; 14:775-785. [PMID: 35536538 DOI: 10.1007/s12539-022-00523-1] [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: 12/02/2021] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 06/14/2023]
Abstract
PURPOSE The identification of potential kinase inhibitors plays a key role in drug discovery for treating human diseases. Currently, most existing computational methods only extract limited features such as sequence information from kinases and inhibitors. To further enhance the identification of kinase inhibitors, more features need to be leveraged. Hence, it is appealing to develop effective methods to aggregate feature information from multisource knowledge for predicting potential kinase inhibitors. In this paper, we propose a novel computational framework called FLMTS to improve the performance of kinase inhibitor prediction by aggregating multisource knowledge. METHOD FLMTS uses a random walk with restart (RWR) to combine multiscale information in a heterogeneous network. We used the combined information as features of compounds and kinases and input them into random forest (RF) to predict unknown compound-kinase interactions. RESULTS Experimental results reveal that FLMTS obtains significant improvement over existing state-of-the-art methods. Case studies demonstrated the reliability of FLMTS, and pathway enrichment analysis demonstrated that FLMTS could also accurately predict signaling pathways in disease treatment. CONCLUSION In conclusion, our computational framework of FLMTS for improving the prediction of potential kinase inhibitors successfully aggregates feature information from multisource knowledge, yielding better prediction performance than existing state-of-the-art methods.
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Affiliation(s)
- Yichen Zhong
- School of Computer Science, University of South China, Hengyang, 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, Hengyang, 421001, China
| | - Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China
| | - Huanhuan Wu
- School of Computer Science, University of South China, Hengyang, 421001, China
| | - Tao Xu
- School of Computer Science, University of South China, Hengyang, 421001, China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, 421001, China.
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, Hengyang, 421001, China.
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Luo J, Bao Y, Chen X, Shen C. Metapath-Based Deep Convolutional Neural Network for Predicting miRNA-Target Association on Heterogeneous Network. Interdiscip Sci 2021; 13:547-558. [PMID: 34170473 DOI: 10.1007/s12539-021-00454-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/17/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Abstract
Predicting the interactions between microRNAs (miRNAs) and target genes is of great significance for understanding the regulatory mechanism of miRNA and treating complex diseases. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for revealing miRNA-associated target genes. However, there are still some limitations about automatically learn the feature information of the network in the existing methods. Since network representation learning can self-adaptively capture structure information of the network, we propose a framework based on heterogeneous network representation, MDCNN (Metapath-Based Deep Convolutional Neural Network), to predict the associations between miRNAs and target genes. MDCNN samples the paths between the node pairs in the form of meta-path based on the heterogeneous information network (HIN) about miRNAs and target genes. Then the node feature and the path feature which is learned by the Deep Convolutional Neural Network (DCNN) are spliced together as the representation of the miRNA-target gene, to predict the miRNA-target gene interactions. The experiment results indicate that the performance of MDCNN outperforms other methods in multiple validation metrics by fivefold cross validation. We set an ablation study to identify the necessity of miRNA similarity and target gene similarity for improving the prediction ability of MDCNN. The case studies on hsa-miR-26b-5p and CDKN1A further demonstrates that MDCNN can successfully predict potential miRNA-target gene interactions.
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Affiliation(s)
- Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China
| | - Yaoting Bao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China
| | - Xiangtao Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China.
| | - Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China
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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.
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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
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Shen C, Luo J, Ouyang W, Ding P, Chen X. IDDkin: Network-based influence deep diffusion model for enhancing prediction of kinase inhibitors. Bioinformatics 2020; 36:5481-5491. [PMID: 33367525 DOI: 10.1093/bioinformatics/btaa1058] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 11/09/2020] [Accepted: 12/10/2020] [Indexed: 01/01/2023] Open
Abstract
MOTIVATION Protein kinases have been the focus of drug discovery research for many years because they play a causal role in many human diseases. Understanding the binding profile of kinase inhibitors is a prerequisite for drug discovery, and traditional methods of predicting kinase inhibitors are time-consuming and inefficient. Calculation-based predictive methods provide a relatively low-cost and high-efficiency approach to the rapid development and effective understanding of the binding profile of kinase inhibitors. Particularly, the continuous improvement of network pharmacology methods provides unprecedented opportunities for drug discovery, network-based computational methods could be employed to aggregate the effective information from heterogeneous sources, which have become a new way for predicting the binding profile of kinase inhibitors. RESULTS In this study, we proposed a network-based influence deep diffusion model, named IDDkin, for enhancing the prediction of kinase inhibitors. IDDkin uses deep graph convolutional networks, graph attention networks and adaptive weighting methods to diffuse the effective information of heterogeneous networks. The updated kinase and compound representations are used to predict potential compound-kinase pairs. The experimental results show that the performance of IDDkin is superior to the comparison methods, including the state-of-the art kinase inhibitor prediction method and the classic model widely used in relationship prediction. In experiments conducted to verify its generalizability and in case studies, the IDDkin model also shows excellent performance. All of these results demonstrate the powerful predictive ability of the IDDkin model in the field of kinase inhibitors. AVAILABILITY AND IMPLEMENTATION Source code and data can be downloaded from https://github.com/ CS-BIO/IDDkin. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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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
| | - Xiangtao Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China
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