1
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Lu P, Jiang J. AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network. Comput Biol Chem 2024; 110:108085. [PMID: 38754260 DOI: 10.1016/j.compbiolchem.2024.108085] [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: 02/28/2024] [Revised: 04/04/2024] [Accepted: 04/23/2024] [Indexed: 05/18/2024]
Abstract
Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-gene-disease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model.
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Affiliation(s)
- Pengli Lu
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
| | - Jicheng Jiang
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
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2
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J SG, P D, P E. Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet. Comput Methods Biomech Biomed Engin 2024:1-18. [PMID: 38375638 DOI: 10.1080/10255842.2023.2282951] [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: 08/01/2023] [Accepted: 10/17/2023] [Indexed: 02/21/2024]
Abstract
Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced data, noise, poor generalization, high cost, and time-consuming processes hinder progress in this field. To overcome the above challenges, we propose a DL-based model termed DrugSchizoNet for drug interaction (DI) prediction of Schizophrenia. Our model leverages drug-related data from the DrugBank and repoDB databases, employing three key preprocessing techniques. First, data cleaning eliminates duplicate or incomplete entries to ensure data integrity. Next, normalization is performed to enhance security and reduce costs associated with data acquisition. Finally, feature extraction is applied to improve the quality of input data. The three layers of the DrugSchizoNet model are the input, hidden and output layers. In the hidden layer, we employ dropout regularization to mitigate overfitting and improve generalization. The fully connected (FC) layer extracts relevant features, while the LSTM layer captures the sequential nature of DIs. In the output layer, our model provides confidence scores for potential DIs. To optimize the prediction accuracy, we utilize hyperparameter tuning through OB-MOA optimization. Experimental results demonstrate that DrugSchizoNet achieves a superior accuracy of 98.70%. The existing models, including CNN-RNN, DANN, CKA-MKL, DGAN, and CNN, across various evaluation metrics such as accuracy, recall, specificity, precision, F1 score, AUPR, and AUROC are compared with the proposed model. By effectively addressing the challenges of imbalanced data, noise, poor generalization, high cost and time-consuming processes, DrugSchizoNet offers a promising approach for accurate DTI prediction in Schizophrenia. Its superior performance demonstrates the potential of DL in advancing drug discovery and development processes.
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Affiliation(s)
- Sherine Glory J
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
| | - Durgadevi P
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
| | - Ezhumalai P
- Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, India
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3
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Sun W, Zhang P, Zhang W, Xu J, Huang Y, Li L. Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction. Interdiscip Sci 2024:10.1007/s12539-023-00602-x. [PMID: 38310628 DOI: 10.1007/s12539-023-00602-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 02/06/2024]
Abstract
MicroRNA (miRNA) serves as a pivotal regulator of numerous cellular processes, and the identification of miRNA-disease associations (MDAs) is crucial for comprehending complex diseases. Recently, graph neural networks (GNN) have made significant advancements in MDA prediction. However, these methods tend to learn one type of node representation from a single heterogeneous network, ignoring the importance of multiple network topologies and node attributes. Here, we propose SMDAP (Sequence hierarchical modeling-based Mirna-Disease Association Prediction framework), a novel GNN-based framework that incorporates multiple network topologies and various node attributes including miRNA seed and full-length sequences to predict potential MDAs. Specifically, SMDAP consists of two types of MDA representation: following a heterogeneous pattern, we construct a transfer learning-like synchronous mutual learning network to learn the first MDA representation in conjunction with the miRNA seed sequence. Meanwhile, following a homogeneous pattern, we design a subgraph-inspired asynchronous multi-scale embedding network to obtain the second MDA representation based on the miRNA full-length sequence. Subsequently, an adaptive fusion approach is designed to combine the two branches such that we can score the MDAs by the downstream classifier and infer novel MDAs. Comprehensive experiments demonstrate that SMDAP integrates the advantages of multiple network topologies and node attributes into two branch representations. Moreover, the area under the receiver operating characteristic curve is 0.9622 on DB1, which is a 5.06% increase from the baselines. The area under the precision-recall curve is 0.9777, which is a 7.33% increase from the baselines. In addition, case studies on three human cancers validated the predictive performance of SMDAP. Overall, SMDAP represents a powerful tool for MDA prediction.
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Affiliation(s)
- Weicheng Sun
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ping Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Weihan Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinsheng Xu
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | | | - Li Li
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China.
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4
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Lin J, Tong X, Li C, Lu Q. Expectile Neural Networks for Genetic Data Analysis of Complex Diseases. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:352-359. [PMID: 35085091 PMCID: PMC10201460 DOI: 10.1109/tcbb.2022.3146795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The genetic etiologies of common diseases are highly complex and heterogeneous. Classic methods, such as linear regression, have successfully identified numerous variants associated with complex diseases. Nonetheless, for most diseases, the identified variants only account for a small proportion of heritability. Challenges remain to discover additional variants contributing to complex diseases. Expectile regression is a generalization of linear regression and provides complete information on the conditional distribution of a phenotype of interest. While expectile regression has many nice properties, it has rarely been used in genetic research. In this paper, we develop an expectile neural network (ENN) method for genetic data analyses of complex diseases. Similar to expectile regression, ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes, which can be used to discover variants predisposing to sub-populations. We further integrate the idea of neural networks into ENN, making it capable of capturing non-linear and non-additive genetic effects (e.g., gene-gene interactions). Through simulations, we showed that the proposed method outperformed an existing expectile regression when there exist complex genotype-phenotype relationships. We also applied the proposed method to the data from the Study of Addiction: Genetics and Environment (SAGE), investigating the relationships of candidate genes with smoking quantity.
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5
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Du XX, Liu Y, Wang B, Zhang JF. lncRNA-disease association prediction method based on the nearest neighbor matrix completion model. Sci Rep 2022; 12:21653. [PMID: 36522410 PMCID: PMC9755128 DOI: 10.1038/s41598-022-25730-0] [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: 08/20/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.
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Affiliation(s)
- Xiao-xin Du
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Yan Liu
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Bo Wang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Jian-fei Zhang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
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Wang L, Wong L, Li Z, Huang Y, Su X, Zhao B, You Z. A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction. Brief Bioinform 2022; 23:6693603. [PMID: 36070867 DOI: 10.1093/bib/bbac388] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/26/2022] [Accepted: 08/11/2022] [Indexed: 11/14/2022] Open
Abstract
Circular RNAs (circRNAs) are involved in the regulatory mechanisms of multiple complex diseases, and the identification of their associations is critical to the diagnosis and treatment of diseases. In recent years, many computational methods have been designed to predict circRNA-disease associations. However, most of the existing methods rely on single correlation data. Here, we propose a machine learning framework for circRNA-disease association prediction, called MLCDA, which effectively fuses multiple sources of heterogeneous information including circRNA sequences and disease ontology. Comprehensive evaluation in the gold standard dataset showed that MLCDA can successfully capture the complex relationships between circRNAs and diseases and accurately predict their potential associations. In addition, the results of case studies on real data show that MLCDA significantly outperforms other existing methods. MLCDA can serve as a useful tool for circRNA-disease association prediction, providing mechanistic insights for disease research and thus facilitating the progress of disease treatment.
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Affiliation(s)
- Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Zhengwei Li
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Yuan Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Xiaorui Su
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Bowei Zhao
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
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Rao Y, Xie M, Wang H. Predict potential miRNA-disease associations based on bounded nuclear norm regularization. Front Genet 2022; 13:978975. [PMID: 36072658 PMCID: PMC9441603 DOI: 10.3389/fgene.2022.978975] [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: 06/27/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Increasing evidences show that the abnormal microRNA (miRNA) expression is related to a variety of complex human diseases. However, the current biological experiments to determine miRNA-disease associations are time consuming and expensive. Therefore, computational models to predict potential miRNA-disease associations are in urgent need. Though many miRNA-disease association prediction methods have been proposed, there is still a room to improve the prediction accuracy. In this paper, we propose a matrix completion model with bounded nuclear norm regularization to predict potential miRNA-disease associations, which is called BNNRMDA. BNNRMDA at first constructs a heterogeneous miRNA-disease network integrating the information of miRNA self-similarity, disease self-similarity, and the known miRNA-disease associations, which is represented by an adjacent matrix. Then, it models the miRNA-disease prediction as a relaxed matrix completion with error tolerance, value boundary and nuclear norm minimization. Finally it implements the alternating direction method to solve the matrix completion problem. BNNRMDA makes full use of available information of miRNAs and diseases, and can deals with the data containing noise. Compared with four state-of-the-art methods, the experimental results show BNNRMDA achieved the best performance in five-fold cross-validation and leave-one-out cross-validation. The case studies on two complex human diseases showed that 47 of the top 50 prediction results of BNNRMDA have been verified in the latest HMDD database.
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Wang L, Wong L, Chen ZH, Hu J, Sun XF, Li Y, You ZH. MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information. BIOLOGY 2022; 11:740. [PMID: 35625468 PMCID: PMC9138588 DOI: 10.3390/biology11050740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 11/25/2022]
Abstract
The key to new drug discovery and development is first and foremost the search for molecular targets of drugs, thus advancing drug discovery and drug repositioning. However, traditional drug-target interactions (DTIs) is a costly, lengthy, high-risk, and low-success-rate system project. Therefore, more and more pharmaceutical companies are trying to use computational technologies to screen existing drug molecules and mine new drugs, leading to accelerating new drug development. In the current study, we designed a deep learning computational model MSPEDTI based on Molecular Structure and Protein Evolutionary to predict the potential DTIs. The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). In cross-validation experiments, MSPEDTI achieved 94.19%, 90.95%, 87.95%, and 86.11% prediction accuracy in the gold-standard datasets enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors, respectively. MSPEDTI showed its competitive ability in ablation experiments and comparison with previous excellent methods. Additionally, 7 of 10 potential DTIs predicted by MSPEDTI were substantiated by the classical database. These excellent outcomes demonstrate the ability of MSPEDTI to provide reliable drug candidate targets and strongly facilitate the development of drug repositioning and drug development.
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Affiliation(s)
- Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
| | - Zhan-Heng Chen
- Computer Science and Technology, Tongji University, Shanghai 200092, China;
| | - Jing Hu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Xiao-Fei Sun
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China;
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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9
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Lou Z, Cheng Z, Li H, Teng Z, Liu Y, Tian Z. Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information. Brief Bioinform 2022; 23:6582005. [PMID: 35524503 DOI: 10.1093/bib/bbac159] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/29/2022] [Accepted: 04/10/2022] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION In recent years, a large number of biological experiments have strongly shown that miRNAs play an important role in understanding disease pathogenesis. The discovery of miRNA-disease associations is beneficial for disease diagnosis and treatment. Since inferring these associations through biological experiments is time-consuming and expensive, researchers have sought to identify the associations utilizing computational approaches. Graph Convolutional Networks (GCNs), which exhibit excellent performance in link prediction problems, have been successfully used in miRNA-disease association prediction. However, GCNs only consider 1st-order neighborhood information at one layer but fail to capture information from high-order neighbors to learn miRNA and disease representations through information propagation. Therefore, how to aggregate information from high-order neighborhood effectively in an explicit way is still challenging. RESULTS To address such a challenge, we propose a novel method called mixed neighborhood information for miRNA-disease association (MINIMDA), which could fuse mixed high-order neighborhood information of miRNAs and diseases in multimodal networks. First, MINIMDA constructs the integrated miRNA similarity network and integrated disease similarity network respectively with their multisource information. Then, the embedding representations of miRNAs and diseases are obtained by fusing mixed high-order neighborhood information from multimodal network which are the integrated miRNA similarity network, integrated disease similarity network and the miRNA-disease association networks. Finally, we concentrate the multimodal embedding representations of miRNAs and diseases and feed them into the multilayer perceptron (MLP) to predict their underlying associations. Extensive experimental results show that MINIMDA is superior to other state-of-the-art methods overall. Moreover, the outstanding performance on case studies for esophageal cancer, colon tumor and lung cancer further demonstrates the effectiveness of MINIMDA. AVAILABILITY AND IMPLEMENTATION https://github.com/chengxu123/MINIMDA and http://120.79.173.96/.
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Affiliation(s)
- Zhengzheng Lou
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Zhaoxu Cheng
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Hui Li
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Yang Liu
- Departments of Cerebrovascular Diseases, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
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Yang L, Li LP, Yi HC. DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph. BMC Bioinformatics 2022; 22:621. [PMID: 35216549 PMCID: PMC8875942 DOI: 10.1186/s12859-022-04579-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/18/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes and have been confirmed to be concerned with various diseases. Largely uncharacterized of the physiological role and functions of lncRNA remains. MicroRNAs (miRNAs), which are usually 20-24 nucleotides, have several critical regulatory parts in cells. LncRNA can be regarded as a sponge to adsorb miRNA and indirectly regulate transcription and translation. Thus, the identification of lncRNA-miRNA associations is essential and valuable. RESULTS In our work, we present DWLMI to infer the potential associations between lncRNAs and miRNAs by representing them as vectors via a lncRNA-miRNA-disease-protein-drug graph. Specifically, DeepWalk can be used to learn the behavior representation of vertices. The methods of fingerprint, k-mer and MeSH descriptors were mainly used to learn the attribute representation of vertices. By combining the above two kinds of information, unknown lncRNA-miRNA associations can be predicted by the random forest classifier. Under the five-fold cross-validation, the proposed DWLMI model obtained an average prediction accuracy of 95.22% with a sensitivity of 94.35% at the AUC of 98.56%. CONCLUSIONS The experimental results demonstrated that DWLMI can effectively predict the potential lncRNA-miRNA associated pairs, and the results can provide a new insight for related non-coding RNA researchers in the field of combing biology big data with deep learning.
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Affiliation(s)
- Long Yang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Li-Ping Li
- College of Grassland and Environmental Science, Xinjiang Agricultural University, Urumqi, 830052, China.
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
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11
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Li Z, Zhong T, Huang D, You ZH, Nie R. Hierarchical graph attention network for miRNA-disease association prediction. Mol Ther 2022; 30:1775-1786. [PMID: 35121109 PMCID: PMC9077381 DOI: 10.1016/j.ymthe.2022.01.041] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/29/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022] Open
Abstract
Many biological studies show that the mutation and abnormal expression of microRNAs (miRNAs) could cause a variety of diseases. As an important biomarker for disease diagnosis, miRNA is helpful to understand pathogenesis, and could promote the identification, diagnosis and treatment of diseases. However, the pathogenic mechanism how miRNAs affect these diseases has not been fully understood. Therefore, predicting the potential miRNA-disease associations is of great importance for the development of clinical medicine and drug research. In this study, we proposed a novel deep learning model based on hierarchical graph attention network for predicting miRNA-disease associations (HGANMDA). Firstly, we constructed a miRNA-disease-lncRNA heterogeneous graph based on known miRNA-disease associations, miRNA-lncRNA associations and disease-lncRNA associations. Secondly, the node-layer attention was applied to learn the importance of neighbor nodes based on different meta-paths. Thirdly, the semantic-layer attention was applied to learn the importance of different meta-paths. Finally, a bilinear decoder was employed to reconstruct the connections between miRNAs and diseases. The extensive experimental results indicated that our model achieved good performance and satisfactory results in predicting miRNA-disease associations.
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12
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Yu DL, Yu ZG, Han GS, Li J, Anh V. Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction. Biomedicines 2021; 9:biomedicines9091152. [PMID: 34572337 PMCID: PMC8465678 DOI: 10.3390/biomedicines9091152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/15/2021] [Accepted: 08/30/2021] [Indexed: 12/02/2022] Open
Abstract
Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any other types of the association for a better understanding of the disease mechanism. It is even more important to reveal associations for currently unassociated miRNAs and diseases. Methods have been recently proposed to make predictions on the association types of miRNA-disease pairs through restricted Boltzman machines, label propagation theories and tensor completion algorithms. None of them has exploited the non-linear characteristics in the miRNA-disease association network to improve the performance. We propose to use attributed multi-layer heterogeneous network embedding to learn the latent representations of miRNAs and diseases from each association type and then to predict the existence of the association type for all the miRNA-disease pairs. The performance of our method is compared with two newest methods via 10-fold cross-validation on the database HMDD v3.2 to demonstrate the superior prediction achieved by our method under different settings. Moreover, our real predictions made beyond the HMDD database can be all validated by NCBI literatures, confirming that our method is capable of accurately predicting new associations of miRNAs with diseases and their association types as well.
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Affiliation(s)
- Dong-Ling Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China; (D.-L.Y.); (G.-S.H.)
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan 411105, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China; (D.-L.Y.); (G.-S.H.)
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan 411105, China
- Correspondence: (Z.-G.Y.); (J.L.)
| | - Guo-Sheng Han
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China; (D.-L.Y.); (G.-S.H.)
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan 411105, China
| | - Jinyan Li
- Data Science Institute, University of Technology Sydney, Broadway, NSW 2007, Australia
- Correspondence: (Z.-G.Y.); (J.L.)
| | - Vo Anh
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia;
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