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Krishnan SR, Roy A, Gromiha MM. Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning. Brief Bioinform 2024; 25:bbae002. [PMID: 38261341 PMCID: PMC10805179 DOI: 10.1093/bib/bbae002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/21/2023] [Accepted: 12/24/2023] [Indexed: 01/24/2024] Open
Abstract
Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.
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Affiliation(s)
- Sowmya R Krishnan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Department of Computer Science, National University of Singapore, Singapore 117543
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2
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Zhong Y, Shen C, Xi X, Luo Y, Ding P, Luo L. Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations. Artif Intell Med 2023; 145:102665. [PMID: 37925217 DOI: 10.1016/j.artmed.2023.102665] [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: 01/11/2023] [Revised: 06/14/2023] [Accepted: 09/14/2023] [Indexed: 11/06/2023]
Abstract
The occurrence of many diseases is associated with miRNA abnormalities. Predicting potential drug-miRNA associations is of great importance for both disease treatment and new drug discovery. Most computation-based approaches learn one task at a time, ignoring the information contained in other tasks in the same domain. Multitask learning can effectively enhance the prediction performance of a single task by extending the valid information of related tasks. In this paper, we presented a multitask joint learning framework (MTJL) with a graph autoencoder for predicting the associations between drugs and miRNAs. First, we combined multiple pieces of information to construct a high-quality similarity network of both drugs and miRNAs and then used a graph autoencoder (GAE) to learn their embedding representations separately. Second, to further improve the embedding quality of drugs, we added an auxiliary task to classify drugs using the learned representations. Finally, the embedding representations of drugs and miRNAs were linearly transformed to obtain the predictive association scores between them. A comparison with other state-of-the-art models shows that MTJL has the best prediction performance, and ablation experiments show that the auxiliary task can enhance the embedding quality and improve the robustness of the model. In addition, we show that MTJL has high utility in predicting potential associations between drugs and miRNAs by conducting two case studies.
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Affiliation(s)
- Yichen Zhong
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Xiaoting Xi
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Yuxun Luo
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411105, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang 421001, China.
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3
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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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4
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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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5
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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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6
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Xie W, Zheng Z, Zhang W, Huang L, Lin Q, Wong KC. SRG-vote: Predicting miRNA-gene relationships via embedding and LSTM ensemble. IEEE J Biomed Health Inform 2022; 26:4335-4344. [PMID: 35471879 DOI: 10.1109/jbhi.2022.3169542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
AbstractTargeted therapy for one for a set of genes has made it possible to apply precision medicine for different patients due to the existence of tumor heterogeneity. However, how to regulate those genes are still problematic. One of the natural regulators of genes is microRNAs. Thus, a better understanding of the miRNA-gene interaction mechanism might contribute to future diagnosis, prevention, and cancer therapy. The interactions between microRNA and genes play an essential role in molecular genetics. The in-vivo experiments validating the relationships between them are time-consuming, money-costly, and labor-intensive. With the development of high-throughput technology, we dealt with tons of biological data. However, extracting features from tremendous raw data and making a mathematical model is still a challenging topic. Machine learning and deep learning algorithms have become powerful tools in dealing with biological data. Inspired by this, in this paper, we propose a model that combines features/embedding extraction methods, deep learning algorithms, and a voting system. We leverage doc2vec to generate sequential embedding from molecular sequences. The role2vec, GCN, and GMM for geometrical embedding were generated from the complex network from similarity and pair-wise datasets. For the deep learning algorithms, we leveraged LSTM and Bi-LSTM according to different embedding and features. Finally, we adopted a voting system to balance results from different data sources. The results have shown that our voting system could achieve a higher AUC than the existing benchmark. The case studies demonstrate that our model could reveal potential relationships between miRNAs and genes. The source code, features, and predictive results can be downloaded at https://github.com/Xshelton/SRG-vote.
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7
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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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8
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Kulichenko M, Smith JS, Nebgen B, Li YW, Fedik N, Boldyrev AI, Lubbers N, Barros K, Tretiak S. The Rise of Neural Networks for Materials and Chemical Dynamics. J Phys Chem Lett 2021; 12:6227-6243. [PMID: 34196559 DOI: 10.1021/acs.jpclett.1c01357] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.
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Affiliation(s)
- Maksim Kulichenko
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322, United States
| | - Justin S Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nikita Fedik
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322, United States
| | - Alexander I Boldyrev
- Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322, United States
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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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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Tang X, Luo J, Shen C, Lai Z. Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction. Brief Bioinform 2021; 22:6271996. [PMID: 33963829 DOI: 10.1093/bib/bbab174] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data. RESULTS In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA-disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction by utilizing multichannel attention, which adaptively learns the importance of different features. Empirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.
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Affiliation(s)
- Xinru Tang
- 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
| | - Cong Shen
- 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
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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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