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Sheng N, Xie X, Wang Y, Huang L, Zhang S, Gao L, Wang H. A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:328-347. [PMID: 38194377 DOI: 10.1109/tcbb.2024.3351752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.
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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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Li ZW, Wang QK, Yuan CA, Han PY, You ZH, Wang L. Predicting MiRNA-Disease Associations by Graph Representation Learning Based on Jumping Knowledge Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2629-2638. [PMID: 35925844 DOI: 10.1109/tcbb.2022.3196394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Growing studies have shown that miRNAs are inextricably linked with many human diseases, and a great deal of effort has been spent on identifying their potential associations. Compared with traditional experimental methods, computational approaches have achieved promising results. In this article, we propose a graph representation learning method to predict miRNA-disease associations. Specifically, we first integrate the verified miRNA-disease associations with the similarity information of miRNA and disease to construct a miRNA-disease heterogeneous graph. Then, we apply a graph attention network to aggregate the neighbor information of nodes in each layer, and then feed the representation of the hidden layer into the structure-aware jumping knowledge network to obtain the global features of nodes. The output features of miRNAs and diseases are then concatenated and fed into a fully connected layer to score the potential associations. Through five-fold cross-validation, the average AUC, accuracy and precision values of our model are 93.30%, 85.18% and 88.90%, respectively. In addition, for three case studies of the esophageal tumor, lymphoma and prostate tumor, 46, 45 and 45 of the top 50 miRNAs predicted by our model were confirmed by relevant databases. Overall, our method could provide a reliable alternative for miRNA-disease association prediction.
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Wong L, Wang L, You ZH, Yuan CA, Huang YA, Cao MY. GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm. BMC Bioinformatics 2023; 24:188. [PMID: 37158823 PMCID: PMC10169329 DOI: 10.1186/s12859-023-05309-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/27/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND The limited knowledge of miRNA-lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments. METHODS In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA-lncRNA interactions (GKLOMLI). Given an observed miRNA-lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA-lncRNA interactions. RESULTS To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method. CONCLUSION GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.
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Affiliation(s)
- Leon Wong
- Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530007, China
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, 200092, Shanghai, China
| | - Lei Wang
- Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530007, China.
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710139, China.
| | - Chang-An Yuan
- Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710139, China
| | - Mei-Yuan Cao
- School of Electrical and Electronic Engineering, Guangdong Technology College, Zhaoqing, 526100, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
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S S, E R V, Krishnakumar U. Improving miRNA Disease Association Prediction Accuracy Using Integrated Similarity Information and Deep Autoencoders. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1125-1136. [PMID: 35914051 DOI: 10.1109/tcbb.2022.3195514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
MicroRNAs (miRNAs) are short endogenous non-encoding RNA molecules (22nt) that have a vital role in many biological and molecular processes inside the human body. Abnormal and dysregulated expressions of miRNAs are correlated with many complex disorders. Time-consuming wet-lab biological experiments are costly and labour-intensive. So, the situation demands feasible and efficient computational approaches for predicting promising miRNAs associated with diseases. Here a two-stage feature pruning approach based on miRNA feature similarity fusion that uses deep attention autoencoder and recursive feature elimination with cross-validation (RFECV) is proposed for predicting unknown miRNA-disease associations. In the first stage, an attention autoencoder captures highly influential features from the fused feature vector. For further pruning of features, RFECV is applied. The resultant features were given to a Random Forest classifier for association prediction. The Highest AUC of 94.41% is attained when all miRNA similarity measures are merged with disease similarities. Case studies were done on two diseases-lymphoma and leukaemia, to examine the reliability of the approach. Comparative analysis shows that the proposed approach outperforms recent methodologies for predicting miRNA-disease associations.
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Liu J, Kuang Z, Deng L. GCNPCA: miRNA-Disease Associations Prediction Algorithm Based on Graph Convolutional Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1041-1052. [PMID: 36049014 DOI: 10.1109/tcbb.2022.3203564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
A growing number of studies have confirmed the important role of microRNAs (miRNAs) in human diseases and the aberrant expression of miRNAs affects the onset and progression of human diseases. The discovery of disease-associated miRNAs as new biomarkers promote the progress of disease pathology and clinical medicine. However, only a small proportion of miRNA-disease correlations have been validated by biological experiments. And identifying miRNA-disease associations through biological experiments is both expensive and inefficient. Therefore, it is important to develop efficient and highly accurate computational methods to predict miRNA-disease associations. A miRNA-disease associations prediction algorithm based on Graph Convolutional neural Networks and Principal Component Analysis (GCNPCA) is proposed in this paper. Specifically, the deep topological structure information is extracted from the heterogeneous network composed of miRNA and disease nodes by a Graph Convolutional neural Network (GCN) with an additional attention mechanism. The internal attribute information of the nodes is obtained by the Principal Component Analysis (PCA). Then, the topological structure information and the node attribute information are combined to construct comprehensive feature descriptors. Finally, the Random Forest (RF) is used to train and classify these feature descriptors. In the five-fold cross-validation experiment, the AUC and AUPR for the GCNPCA algorithm are 0.983 and 0.988 respectively.
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Feng H, Jin D, Li J, Li Y, Zou Q, Liu T. Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations. Brief Bioinform 2023; 24:6960615. [PMID: 36567252 DOI: 10.1093/bib/bbac571] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/16/2022] [Accepted: 11/23/2022] [Indexed: 12/27/2022] Open
Abstract
Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.
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Affiliation(s)
- Hailin Feng
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Dongdong Jin
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Jian Li
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Yane Li
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West District, high tech Zone, 611731, Chengdu, China
| | - Tongcun Liu
- School of mathematics and computer science, Zhejiang A&F University, No.666 Wusu Street,Lin'an District, 311300, Hangzhou, China
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Zheng K, Zhang XL, Wang L, You ZH, Zhan ZH, Li HY. Line graph attention networks for predicting disease-associated Piwi-interacting RNAs. Brief Bioinform 2022; 23:6748487. [PMID: 36198846 DOI: 10.1093/bib/bbac393] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/08/2022] [Accepted: 08/12/2022] [Indexed: 12/14/2022] Open
Abstract
PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.
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Affiliation(s)
- Kai Zheng
- College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China.,Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | | | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China.,Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
| | - Zhao-Hui Zhan
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Hao-Yuan Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
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Dong N, Mucke S, Khosla M. MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3081-3092. [PMID: 35594217 DOI: 10.1109/tcbb.2022.3176456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Growing evidence from recent studies implies that microRNAs or miRNAs could serve as biomarkers in various complex human diseases. Since wet-lab experiments for detecting miRNAs associated with a disease are expensive and time-consuming, machine learning techniques for miRNA-disease association prediction have attracted much attention in recent years. A big challenge in building reliable machine learning models is that of data scarcity. In particular, existing approaches trained on the available small datasets, even when combined with precalculated handcrafted input features, often suffer from bad generalization and data leakage problems. We overcome the limitations of existing works by proposing a novel multitask graph convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from five heterogeneous biological information sources (associations between miRNAs/diseases and protein-coding genes (PCGs), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multitask setting which is a novel perspective and has not been studied before. To effectively test the generalization capability of our model, we conduct large-scale experiments on the standard benchmark datasets as well as on our proposed large independent testing sets and case studies. MuCoMiD obtains significantly higher Average Precision (AP) scores than all benchmarked models on three large independent testing sets, especially those with many new miRNAs, as well as in the detection of false positives. Thanks to its capability of learning directly from raw input information, MuCoMiD is easier to maintain and update than handcrafted feature-based methods, which would require recomputation of features every time there is a change in the original information sources (e.g., disease ontology, miRNA/disease-PCG associations, etc.). We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/cmtt.
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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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Lu S, Liang Y, Li L, Liao S, Ouyang D. Inferring human miRNA–disease associations via multiple kernel fusion on GCNII. Front Genet 2022; 13:980497. [PMID: 36134032 PMCID: PMC9483142 DOI: 10.3389/fgene.2022.980497] [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: 06/30/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Increasing evidence shows that the occurrence of human complex diseases is closely related to the mutation and abnormal expression of microRNAs(miRNAs). MiRNAs have complex and fine regulatory mechanisms, which makes it a promising target for drug discovery and disease diagnosis. Therefore, predicting the potential miRNA-disease associations has practical significance. In this paper, we proposed an miRNA–disease association predicting method based on multiple kernel fusion on Graph Convolutional Network via Initial residual and Identity mapping (GCNII), called MKFGCNII. Firstly, we built a heterogeneous network of miRNAs and diseases to extract multi-layer features via GCNII. Secondly, multiple kernel fusion method was applied to weight fusion of embeddings at each layer. Finally, Dual Laplacian Regularized Least Squares was used to predict new miRNA–disease associations by the combined kernel in miRNA and disease spaces. Compared with the other methods, MKFGCNII obtained the highest AUC value of 0.9631. Code is available at https://github.com/cuntjx/bioInfo.
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Affiliation(s)
- Shanghui Lu
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
- School of Mathematics and Physics, Hechi University, Hechi, China
| | - Yong Liang
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
- Peng Cheng Laboratory, Shenzhen, China
- *Correspondence: Yong Liang,
| | - Le Li
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
| | - Shuilin Liao
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
| | - Dong Ouyang
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
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12
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Ma M, Na S, Zhang X, Chen C, Xu J. SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction. Brief Bioinform 2022; 23:6678419. [PMID: 36037084 DOI: 10.1093/bib/bbac340] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/21/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However, the existing GNN-based methods have over-smoothing issue-the learned feature embeddings of miRNA nodes and disease nodes are indistinguishable when stacking multiple GNN layers. This issue makes the performance of the methods sensitive to the number of layers, and significantly hurts the performance when more layers are employed. In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE. The key novelty of SFGAE is to construct miRNA-self embeddings and disease-self embeddings, and let them be independent of graph interactions between two types of nodes. The novel self-feature embeddings enrich the information of typical aggregated feature embeddings, which aggregate the information from direct neighbors and hence heavily rely on graph interactions. SFGAE adopts a graph encoder with attention mechanism to concatenate aggregated feature embeddings and self-feature embeddings, and adopts a bilinear decoder to predict links. Our experiments show that SFGAE achieves state-of-the-art performance. In particular, SFGAE improves the average AUC upon recent GAEMDA [1] on the benchmark datasets HMDD v2.0 and HMDD v3.2, and consistently performs better when less (e.g. 10%) training samples are used. Furthermore, SFGAE effectively overcomes the over-smoothing issue and performs stably well on deeper models (e.g. eight layers). Finally, we carry out case studies on three human diseases, colon neoplasms, esophageal neoplasms and kidney neoplasms, and perform a survival analysis using kidney neoplasm as an example. The results suggest that SFGAE is a reliable tool for predicting potential miRNA-disease associations.
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Affiliation(s)
- Mingyuan Ma
- Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, China
| | - Sen Na
- International Computer Science Institute and Department of Statistics, University of California, Berkeley, Berkeley CA, USA
| | - Xiaolu Zhang
- Department of Information Systems, City University of Hong Kong, Hong Kong, China
| | - Congzhou Chen
- Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, China
| | - Jin Xu
- Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, China
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Wang Y, Wang LL, Wong L, Li Y, Wang L, You ZH. SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks. Biomedicines 2022; 10:biomedicines10071543. [PMID: 35884848 PMCID: PMC9313220 DOI: 10.3390/biomedicines10071543] [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: 05/26/2022] [Revised: 06/24/2022] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Protein is the basic organic substance that constitutes the cell and is the material condition for the life activity and the guarantee of the biological function activity. Elucidating the interactions and functions of proteins is a central task in exploring the mysteries of life. As an important protein interaction, self-interacting protein (SIP) has a critical role. The fast growth of high-throughput experimental techniques among biomolecules has led to a massive influx of available SIP data. How to conduct scientific research using the massive amount of SIP data has become a new challenge that is being faced in related research fields such as biology and medicine. In this work, we design an SIP prediction method SIPGCN using a deep learning graph convolutional network (GCN) based on protein sequences. First, protein sequences are characterized using a position-specific scoring matrix, which is able to describe the biological evolutionary message, then their hidden features are extracted by the deep learning method GCN, and, finally, the random forest is utilized to predict whether there are interrelationships between proteins. In the cross-validation experiment, SIPGCN achieved 93.65% accuracy and 99.64% specificity in the human data set. SIPGCN achieved 90.69% and 99.08% of these two indicators in the yeast data set, respectively. Compared with other feature models and previous methods, SIPGCN showed excellent results. These outcomes suggest that SIPGCN may be a suitable instrument for predicting SIP and may be a reliable candidate for future wet experiments.
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Affiliation(s)
- Ying Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China;
| | - Lin-Lin Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China;
- Correspondence: (L.-L.W.); (L.W.)
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China; (L.W.); (Z.-H.Y.)
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China;
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China;
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China; (L.W.); (Z.-H.Y.)
- Correspondence: (L.-L.W.); (L.W.)
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China; (L.W.); (Z.-H.Y.)
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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14
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Ji BY, Pan LR, Zhou JR, You ZH, Peng SL. SMMDA: Predicting miRNA-Disease Associations by Incorporating Multiple Similarity Profiles and a Novel Disease Representation. BIOLOGY 2022; 11:biology11050777. [PMID: 35625505 PMCID: PMC9138858 DOI: 10.3390/biology11050777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/17/2022] [Accepted: 05/17/2022] [Indexed: 12/24/2022]
Abstract
Simple Summary Predicting possible associations between miRNAs and diseases would provide new perspectives on disease diagnosis, pathogenesis, and gene therapy. In this work, considering the limited accessibility, high time consumption and high cost in traditional biological researches, we presented a novel computational method called SMMDA by incorporating multiple similarity profiles and a novel disease rep-resentation to accelerate the identification of potential miRNA-disease associations. SMMDA was intended to be useful for the prediction of associations between miRNAs and diseases, and to be effective for prevention, diagnosis, treatment and prognosis of Human diseases. Abstract Increasing evidence has suggested that microRNAs (miRNAs) are significant in research on human diseases. Predicting possible associations between miRNAs and diseases would provide new perspectives on disease diagnosis, pathogenesis, and gene therapy. However, considering the intrinsic time-consuming and expensive cost of traditional Vitro studies, there is an urgent need for a computational approach that would allow researchers to identify potential associations between miRNAs and diseases for further research. In this paper, we presented a novel computational method called SMMDA to predict potential miRNA-disease associations. In particular, SMMDA first utilized a new disease representation method (MeSHHeading2vec) based on the network embedding algorithm and then fused it with Gaussian interaction profile kernel similarity information of miRNAs and diseases, disease semantic similarity, and miRNA functional similarity. Secondly, SMMDA utilized a deep auto-coder network to transform the original features further to achieve a better feature representation. Finally, the ensemble learning model, XGBoost, was used as the underlying training and prediction method for SMMDA. In the results, SMMDA acquired a mean accuracy of 86.68% with a standard deviation of 0.42% and a mean AUC of 94.07% with a standard deviation of 0.23%, outperforming many previous works. Moreover, we also compared the predictive ability of SMMDA with different classifiers and different feature descriptors. In the case studies of three common Human diseases, the top 50 candidate miRNAs have 47 (esophageal neoplasms), 48 (breast neoplasms), and 48 (colon neoplasms) are successfully verified by two other databases. The experimental results proved that SMMDA has a reliable prediction ability in predicting potential miRNA-disease associations. Therefore, it is anticipated that SMMDA could be an effective tool for biomedical researchers.
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Affiliation(s)
- Bo-Ya Ji
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410200, China; (B.-Y.J.); (L.-R.P.)
| | - Liang-Rui Pan
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410200, China; (B.-Y.J.); (L.-R.P.)
| | - Ji-Ren Zhou
- College of Computer Science, Northwestern Polytechnic University, Xi’an 710072, China;
| | - Zhu-Hong You
- College of Computer Science, Northwestern Polytechnic University, Xi’an 710072, China;
- Correspondence: (Z.-H.Y.); (S.-L.P.)
| | - Shao-Liang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410200, China; (B.-Y.J.); (L.-R.P.)
- Correspondence: (Z.-H.Y.); (S.-L.P.)
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15
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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: 21] [Impact Index Per Article: 10.5] [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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16
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Li X, Ai H, Li B, Zhang C, Meng F, Ai Y. MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to Identify Key miRNAs (microRNAs). Front Genet 2022; 13:825318. [PMID: 35154284 PMCID: PMC8829120 DOI: 10.3389/fgene.2022.825318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/10/2022] [Indexed: 01/22/2023] Open
Abstract
Identifying cancer-related miRNAs (or microRNAs) that precisely target mRNAs is important for diagnosis and treatment of cancer. Creating novel methods to identify candidate miRNAs becomes an imminent Frontier of researches in the field. One major obstacle lies in the integration of the state-of-the-art databases. Here, we introduce a novel method, MIMRDA, which incorporates the miRNA and mRNA expression profiles for predicting miRNA-disease associations to identify key miRNAs. As a proof-of-principle study, we use the MIMRDA method to analyze TCGA datasets of 20 types (BLCA, BRCA, CESE, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, THCA and UCEC) of cancer, which identified hundreds of top-ranked miRNAs. Some (as Category 1) of them are endorsed by public databases including TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR and mTD; others (as Category 2) are supported by literature evidences. miR-21 (representing Category 1) and miR-1258 (representing Category 2) display the excellent characteristics of biomarkers in multi-dimensional assessments focusing on the function similarity analysis, overall survival analysis, and anti-cancer drugs’ sensitivity or resistance analysis. We compare the performance of the MIMRDA method over the Limma and SPIA packages, and estimate the accuracy of the MIMRDA method in classifying top-ranked miRNAs via the Random Forest simulation test. Our results indicate the superiority and effectiveness of the MIMRDA method, and recommend some top-ranked key miRNAs be potential biomarkers that warrant experimental validations.
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Affiliation(s)
- Xianbin Li
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Hannan Ai
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Department of Electrical and Computer Engineering, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- National Center for Quality Supervision and Inspection of Automatic Equipment, National Center for Testing and Evaluation of Robots (Guangzhou), CRAT, SINOMACH-IT, Guangzhou, China
- *Correspondence: Yuncan Ai, ; Hannan Ai,
| | - Bizhou Li
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Chaohui Zhang
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Fanmei Meng
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yuncan Ai
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Yuncan Ai, ; Hannan Ai,
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17
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Jiang H, Huang Y. An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network. BMC Bioinformatics 2022; 23:9. [PMID: 34983364 PMCID: PMC8726520 DOI: 10.1186/s12859-021-04553-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. RESULTS In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. CONCLUSIONS The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.
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Affiliation(s)
- Hanjing Jiang
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, Institute of Artificial Intelligence, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yabing Huang
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
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18
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Uthayopas K, de Sá AGC, Alavi A, Pires DEV, Ascher DB. TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction. MOLECULAR THERAPY. NUCLEIC ACIDS 2021; 26:536-546. [PMID: 34631283 PMCID: PMC8479276 DOI: 10.1016/j.omtn.2021.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/19/2021] [Indexed: 02/06/2023]
Abstract
The emergence of high-throughput sequencing techniques has revealed a primary role of microRNAs (miRNAs) in a wide range of diseases, including cancers and neurodegenerative disorders. Understanding novel relationships between miRNAs and diseases can potentially unveil complex pathogenesis mechanisms, leading to effective diagnosis and treatment. The investigation of novel miRNA-disease associations, however, is currently costly and time consuming. Over the years, several computational models have been proposed to prioritize potential miRNA-disease associations, but with limited usability or predictive capability. In order to fill this gap, we introduce TSMDA, a novel machine-learning method that leverages target and symptom information and negative sample selection to predict miRNA-disease association. TSMDA significantly outperforms similar methods, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 and 0.982 under 5-fold cross-validation and blind test, respectively. We also demonstrate the capability of the method to uncover potential miRNA-disease associations in breast, prostate, and lung cancers, as case studies. We believe TSMDA will be an invaluable tool for the community to explore and prioritize potentially new miRNA-disease associations for further experimental characterization. The method was made available as a freely accessible and user-friendly web interface at http://biosig.unimelb.edu.au/tsmda/.
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Affiliation(s)
- Korawich Uthayopas
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, VIC, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, VIC, Australia
| | - Alex G C de Sá
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, VIC, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, VIC, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, VIC, Australia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, VIC, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, VIC, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, VIC, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, VIC, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, VIC, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Parkville 3052, VIC, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, VIC, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, VIC, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, VIC, Australia.,Department of Biochemistry, University of Cambridge, 80 Tennis Ct Rd, Cambridge CB2 1GA, UK
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19
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Wang L, You ZH, Li JQ, Huang YA. IMS-CDA: Prediction of CircRNA-Disease Associations From the Integration of Multisource Similarity Information With Deep Stacked Autoencoder Model. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:5522-5531. [PMID: 33027025 DOI: 10.1109/tcyb.2020.3022852] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Emerging evidence indicates that circular RNA (circRNA) has been an indispensable role in the pathogenesis of human complex diseases and many critical biological processes. Using circRNA as a molecular marker or therapeutic target opens up a new avenue for our treatment and detection of human complex diseases. The traditional biological experiments, however, are usually limited to small scale and are time consuming, so the development of an effective and feasible computational-based approach for predicting circRNA-disease associations is increasingly favored. In this study, we propose a new computational-based method, called IMS-CDA, to predict potential circRNA-disease associations based on multisource biological information. More specifically, IMS-CDA combines the information from the disease semantic similarity, the Jaccard and Gaussian interaction profile kernel similarity of disease and circRNA, and extracts the hidden features using the stacked autoencoder (SAE) algorithm of deep learning. After training in the rotation forest (RF) classifier, IMS-CDA achieves 88.08% area under the ROC curve with 88.36% accuracy at the sensitivity of 91.38% on the CIRCR2Disease dataset. Compared with the state-of-the-art support vector machine and K -nearest neighbor models and different descriptor models, IMS-CDA achieves the best overall performance. In the case studies, eight of the top 15 circRNA-disease associations with the highest prediction score were confirmed by recent literature. These results indicated that IMS-CDA has an outstanding ability to predict new circRNA-disease associations and can provide reliable candidates for biological experiments.
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20
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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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21
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Zheng K, You ZH, Wang L, Li YR, Zhou JR, Zeng HT. MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1733-1742. [PMID: 32749964 DOI: 10.1109/tcbb.2020.3013837] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In the past few years, the prediction models have shown remarkable performance in most biological correlation prediction tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. These models often encounter training issues such as sensitivity to hyperparameter tuning and "catastrophic forgetting" when adding new data. However, with the development of biomedicine and the accumulation of biological data, new predictive models are required to face the challenge of adapting to change. To this end, we propose a computational approach based on Broad learning system (BLS) to predict potential disease-associated miRNAs that retain the ability to distinguish prior training associations when new data need to be adapted. In particular, we are introducing incremental learning to the field of biological association prediction for the first time and proposed a new method for quantifying sequence similarity. In the performance evaluation, the AUC in the 5-fold cross-validation was 0.9400 +/- 0.0041. To better assess the effectiveness of MISSIM, we compared it with various classifiers and former prediction models. Its performance is superior to the previous method. Besides, the case study on identifying miRNAs associated with breast neoplasms, lung neoplasms and esophageal neoplasms show that 34, 36 and 35 out of the top 40 associations predicted by MISSIM are confirmed by recent biomedical resources. These results provide ample convincing evidence of this approach have potential value and prospect in promoting biomedical research productivity.
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22
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Dai Q, Chu Y, Li Z, Zhao Y, Mao X, Wang Y, Xiong Y, Wei DQ. MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information. Comput Biol Med 2021; 136:104706. [PMID: 34371319 DOI: 10.1016/j.compbiomed.2021.104706] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 01/17/2023]
Abstract
MicroRNAs (miRNAs) are significant regulators in various biological processes. They may become promising biomarkers or therapeutic targets, which provide a new perspective in diagnosis and treatment of multiple diseases. Since the experimental methods are always costly and resource-consuming, prediction of disease-related miRNAs using computational methods is in great need. In this study, we developed MDA-CF to identify underlying miRNA-disease associations based on a cascade forest model. In this method, multi-source information was integrated to represent miRNAs and diseases comprehensively, and the autoencoder was utilized for dimension reduction to obtain the optimal feature space. The cascade forest model was then employed for miRNA-disease association prediction. As a result, the average AUC of MDA-CF was 0.9464 on HMDD v3.2 in five-fold cross-validation. Compared with previous computational methods, MDA-CF performed better on HMDD v2.0 with an average AUC of 0.9258. Moreover, MDA-CF was implemented to investigate colon neoplasm, breast neoplasm, and gastric neoplasm, and 100%, 86%, 88% of the top 50 potential miRNAs were validated by authoritative databases. In conclusion, MDA-CF appears to be a reliable method to uncover disease-associated miRNAs. The source code of MDA-CF is available at https://github.com/a1622108/MDA-CF.
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Affiliation(s)
- Qiuying Dai
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhiqi Li
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yusong Zhao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, China.
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23
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Ji BY, You ZH, Wang Y, Li ZW, Wong L. DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding. iScience 2021; 24:102455. [PMID: 34041455 PMCID: PMC8141887 DOI: 10.1016/j.isci.2021.102455] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 03/02/2021] [Accepted: 04/19/2021] [Indexed: 12/24/2022] Open
Abstract
Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments. In this study, we propose a computational machine learning-based method (DANE-MDA) that preserves integrated structure and attribute features via deep attributed network embedding to predict potential miRNA-disease associations. Specifically, the integrated features are extracted by using deep stacked auto-encoder on the diverse orders of matrixes containing structure and attribute information and are then trained by using random forest classifier. Under 5-fold cross-validation experiments, DANE-MDA yielded average accuracy, sensitivity, and AUC at 85.59%, 84.23%, and 0.9264 in term of HMDD v3.0 dataset, and 83.21%, 80.39%, and 0.9113 in term of HMDD v2.0 dataset, respectively. Additionally, case studies on breast, colon, and lung neoplasms related disease show that 47, 47, and 46 of the top 50 miRNAs can be predicted and retrieved in the other database. A computational machine learning-based method for miRNA-disease association prediction Preserve structure and attribute features via deep attributed network embedding Capture the interaction between two kinds of features from diverse degrees of proximity Extract the higher-order features via deep stacked auto-encoder neural network
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Affiliation(s)
- Bo-Ya Ji
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of the Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of the Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Yi Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Leon Wong
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of the Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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24
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Li HY, Chen HY, Wang L, Song SJ, You ZH, Yan X, Yu JQ. A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network. Sci Rep 2021; 11:12640. [PMID: 34135401 PMCID: PMC8209151 DOI: 10.1038/s41598-021-91991-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 04/30/2021] [Indexed: 02/05/2023] Open
Abstract
Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.
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Affiliation(s)
- Hao-Yuan Li
- grid.411510.00000 0000 9030 231XSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 China
| | - Hai-Yan Chen
- Xinjiang Autonomous Region tax Service, State Taxation Administration, Urumqi, 830011 China
| | - Lei Wang
- grid.9227.e0000000119573309Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011 China
| | - Shen-Jian Song
- Science & Technology Department of Xinjiang Uygur Autonomous Region, Urumqi, 830011 China
| | - Zhu-Hong You
- grid.9227.e0000000119573309Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011 China
| | - Xin Yan
- grid.411510.00000 0000 9030 231XSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 China
| | - Jin-Qian Yu
- grid.411510.00000 0000 9030 231XSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 China
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Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Brief Bioinform 2021; 22:6261915. [PMID: 34009265 DOI: 10.1093/bib/bbab165] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Xuhong Wang
- School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, China
| | - Qiuying Dai
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, China
| | | | | | - Dennis Russell Salahub
- Department of Chemistry, University of Calgary, Fellow Royal Society of Canada and Fellow of the American Association for the Advancement of Science, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
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26
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Tang M, Liu C, Liu D, Liu J, Liu J, Deng L. PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction. Front Genet 2021; 12:656107. [PMID: 33897768 PMCID: PMC8063614 DOI: 10.3389/fgene.2021.656107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 02/18/2021] [Indexed: 12/23/2022] Open
Abstract
MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.
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Affiliation(s)
- Mingyan Tang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Chenzhe Liu
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Dayun Liu
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Junyi Liu
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Jiaqi Liu
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
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27
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Li HY, You ZH, Wang L, Yan X, Li ZW. DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association. Mol Ther 2021; 29:1501-1511. [PMID: 33429082 DOI: 10.1016/j.ymthe.2021.01.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 12/21/2020] [Accepted: 01/01/2021] [Indexed: 12/28/2022] Open
Abstract
It is reported that microRNAs (miRNAs) play an important role in various human diseases. However, the mechanisms of miRNA in these diseases have not been fully understood. Therefore, detecting potential miRNA-disease associations has far-reaching significance for pathological development and the diagnosis and treatment of complex diseases. In this study, we propose a novel diffusion-based computational method, DF-MDA, for predicting miRNA-disease association based on the assumption that molecules are related to each other in human physiological processes. Specifically, we first construct a heterogeneous network by integrating various known associations among miRNAs, diseases, proteins, long non-coding RNAs (lncRNAs), and drugs. Then, more representative features are extracted through a diffusion-based machine-learning method. Finally, the Random Forest classifier is adopted to classify miRNA-disease associations. In the 5-fold cross-validation experiment, the proposed model obtained the average area under the curve (AUC) of 0.9321 on the HMDD v3.0 dataset. To further verify the prediction performance of the proposed model, DF-MDA was applied in three significant human diseases, including lymphoma, lung neoplasms, and colon neoplasms. As a result, 47, 46, and 47 out of top 50 predictions were validated by independent databases. These experimental results demonstrated that DF-MDA is a reliable and efficient method for predicting potential miRNA-disease associations.
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Affiliation(s)
- Hao-Yuan Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
| | - Lei Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Xin Yan
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China.
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
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28
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Wang L, Yan X, You ZH, Zhou X, Li HY, Huang YA. SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations. Brief Bioinform 2021; 22:6175330. [PMID: 33734296 DOI: 10.1093/bib/bbab028] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 12/31/2022] Open
Abstract
Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biological data to identify the association between circRNA and disease can effectively reduce cost and save time. Considering the limitations of existing computational models, we propose a semi-supervised generative adversarial network (GAN) model SGANRDA for predicting circRNA-disease association. This model first fused the natural language features of the circRNA sequence and the features of disease semantics, circRNA and disease Gaussian interaction profile kernel, and then used all circRNA-disease pairs to pre-train the GAN network, and fine-tune the network parameters through labeled samples. Finally, the extreme learning machine classifier is employed to obtain the prediction result. Compared with the previous supervision model, SGANRDA innovatively introduced circRNA sequences and utilized all the information of circRNA-disease pairs during the pre-training process. This step can increase the information content of the feature to some extent and reduce the impact of too few known associations on the model performance. SGANRDA obtained AUC scores of 0.9411 and 0.9223 in leave-one-out cross-validation and 5-fold cross-validation, respectively. Prediction results on the benchmark dataset show that SGANRDA outperforms other existing models. In addition, 25 of the top 30 circRNA-disease pairs with the highest scores of SGANRDA in case studies were verified by recent literature. These experimental results demonstrate that SGANRDA is a useful model to predict the circRNA-disease association and can provide reliable candidates for biological experiments.
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Affiliation(s)
- Lei Wang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
| | - Xin Yan
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
| | - Xi Zhou
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
| | - Hao-Yuan Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
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29
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Jia LN, Yan X, You ZH, Zhou X, Li LP, Wang L, Song KJ. NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information. Evol Bioinform Online 2020; 16:1176934320984171. [PMID: 33488064 PMCID: PMC7768313 DOI: 10.1177/1176934320984171] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 12/01/2020] [Indexed: 12/13/2022] Open
Abstract
The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments.
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Affiliation(s)
- Li-Na Jia
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
| | - Xin Yan
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
- School of Foreign Languages, Zaozhuang University, Zaozhuang, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Xi Zhou
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Li-Ping Li
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
- Lei Wang, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
| | - Ke-Jian Song
- School of information engineering, Jiangxi University of Science and Technology, Ganzhou, China
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30
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Ji BY, You ZH, Chen ZH, Wong L, Yi HC. NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. BMC Bioinformatics 2020; 21:401. [PMID: 32912137 PMCID: PMC7646193 DOI: 10.1186/s12859-020-03716-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 08/19/2020] [Indexed: 12/25/2022] Open
Abstract
Background As an important non-coding RNA, microRNA (miRNA) plays a significant role in a series of life processes and is closely associated with a variety of Human diseases. Hence, identification of potential miRNA-disease associations can make great contributions to the research and treatment of Human diseases. However, to our knowledge, many existing computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. Results In this paper, we propose a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. Firstly, a heterogeneous network is constructed by integrating known associations among miRNA, protein and disease, and the network representation method Learning Graph Representations with Global Structural Information (GraRep) is implemented to learn the behavior information of miRNAs and diseases in the network. Then, the behavior information of miRNAs and diseases is combined with the attribute information of them to represent miRNA-disease association pairs. Finally, the prediction model is established based on the Random Forest algorithm. Under the five-fold cross validation, the proposed NEMPD model obtained average 85.41% prediction accuracy with 80.96% sensitivity at the AUC of 91.58%. Furthermore, the performance of NEMPD is also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. Conclusions The proposed NEMPD model has a good performance in predicting the potential associations between miRNAs and diseases, and has great potency in the field of miRNA-disease association prediction in the future.
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Affiliation(s)
- Bo-Ya Ji
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Zhan-Heng Chen
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Leon Wong
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hai-Cheng Yi
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
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31
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Zheng K, You ZH, Li JQ, Wang L, Guo ZH, Huang YA. iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation. PLoS Comput Biol 2020; 16:e1007872. [PMID: 32421715 PMCID: PMC7259804 DOI: 10.1371/journal.pcbi.1007872] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 05/29/2020] [Accepted: 04/13/2020] [Indexed: 12/14/2022] Open
Abstract
Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments. Understanding the association between circRNAs and diseases is an important step to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Computational methods contribute to discovering the potential disease-related circRNAs. Based on the analysis of the location information expression of biological sequences, the model of iCDA-CGR is proposed to predict the circRNA-disease associations by integrates multi-source information, including circRNA sequence information, gene-circRNA associations information, circRNA-disease associations information and the disease semantic information. In particular, the location information of circRNA sequences was first introduced into the circRNA-disease associations prediction model. The promising results on cross-validation and independent data sets demonstrated the effectiveness of the proposed model. We further implemented case studies, and 19 of the top 30 predicted scores of the proposed model were confirmed by recent experimental reports. The results show that iCDA-CGR model can effectively predict the potential circRNA-disease associations and provide highly reliable candidates for biological experiments, thus helping to further understand the complex disease mechanism.
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Affiliation(s)
- Kai Zheng
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- * E-mail: (ZHY); (LW)
| | - Jian-Qiang Li
- College of Computer and Software Engineering, Shenzhen University, Shenzhen, China
| | - Lei Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
- * E-mail: (ZHY); (LW)
| | - Zhen-Hao Guo
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China
| | - Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
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32
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Yan C, Wu FX, Wang J, Duan G. PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences. BMC Bioinformatics 2020; 21:111. [PMID: 32183740 PMCID: PMC7079416 DOI: 10.1186/s12859-020-3426-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/21/2020] [Indexed: 11/16/2022] Open
Abstract
Background MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.
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Affiliation(s)
- Cheng Yan
- Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China.,School of Computer and Information,Qiannan Normal University for Nationalities, Longshan Road, DuYun, 558000, China
| | - Fang-Xiang Wu
- Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SKS7N5A9, Canada
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China
| | - Guihua Duan
- Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China.
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