1
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Lu P, Wu J, Zhang W. Identifying circRNA-disease association based on relational graph attention network and hypergraph attention network. Anal Biochem 2024; 694:115628. [PMID: 39069246 DOI: 10.1016/j.ab.2024.115628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 07/11/2024] [Accepted: 07/18/2024] [Indexed: 07/30/2024]
Abstract
In recent years, with the in-depth study of circRNA, scholars have begun to discover a synergistic relationship between circRNA and microorganisms. Traditional wet lab experiments in biology require expensive financial, material, and human resources to investigate the relationship between circRNA and diseases. Therefore, we propose a new predictive model for inferring the association between circRNA and diseases, called HAGACDA. Specifically, we first aggregate the unique features of circRNA and diseases themselves through singular value decomposition, Pearson similarity, and the biological information characteristics of circRNA and diseases. Utilizing the competitive relationships between miRNA and other microorganisms, we construct a circRNA-miRNA-disease multi-source heterogeneous network. Subsequently, we use a relational graph attention network to aggregate features based on the structural connections between different nodes. To address the inherent limitations in capturing high-order patterns in edge sets, we integrate a hypergraph attention network to extract features of circRNA and diseases. Finally, association prediction scores for node pairs are obtained through a multilayer perceptron. We conducted a comprehensive analysis of the model, including comparative experiments and case studies. Experimental results demonstrate that our model accurately predicts the association between circRNA and diseases.
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Affiliation(s)
- PengLi Lu
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
| | - Jinkai Wu
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
| | - Wenqi Zhang
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
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2
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Yuan L, Zhao L, Lai J, Jiang Y, Zhang Q, Shen Z, Zheng CH, Huang DS. iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites. PLoS Comput Biol 2024; 20:e1012399. [PMID: 39173070 PMCID: PMC11373821 DOI: 10.1371/journal.pcbi.1012399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 09/04/2024] [Accepted: 08/08/2024] [Indexed: 08/24/2024] Open
Abstract
Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites.
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Affiliation(s)
- Lin Yuan
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Ling Zhao
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Jinling Lai
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Yufeng Jiang
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Qinhu Zhang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China
| | - Zhen Shen
- School of Computer and Software, Nanyang Institute of Technology, Nanyang, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
| | - De-Shuang Huang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China
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3
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Chen Q, Qiu J, Lan W, Cao J. Similarity-guided graph contrastive learning for lncRNA-disease association prediction. J Mol Biol 2024:168609. [PMID: 38750722 DOI: 10.1016/j.jmb.2024.168609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.
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Affiliation(s)
- Qingfeng Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junlai Qiu
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Wei Lan
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junyue Cao
- College of Life Science and Technology, Guangxi University, Nanning 530004, Guangxi, China.
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4
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Kang WY, Gao YL, Wang Y, Li F, Liu JX. KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder. IEEE J Biomed Health Inform 2024; 28:3178-3185. [PMID: 38408006 DOI: 10.1109/jbhi.2024.3369650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes. To solve these problems, this paper proposes a novel method based on Kernel Fusion and Deep Auto-encoder (KFDAE) to predict the potential associations between circRNAs and diseases. Firstly, KFDAE uses a non-linear method to fuse the circRNA similarity kernels and disease similarity kernels. Then the vectors are connected to make the positive and negative sample sets, and these data are send to deep auto-encoder to reduce dimension and extract features. Finally, three-layer deep feedforward neural network is used to learn features and gain the prediction score. The experimental results show that compared with existing methods, KFDAE achieves the best performance. In addition, the results of case studies prove the effectiveness and practical significance of KFDAE, which means KFDAE is able to capture more comprehensive information and generate credible candidate for subsequent wet-lab.
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5
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Yang J, Lei X, Zhang F. Identification of circRNA-disease associations via multi-model fusion and ensemble learning. J Cell Mol Med 2024; 28:e18180. [PMID: 38506066 PMCID: PMC10951890 DOI: 10.1111/jcmm.18180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/21/2024] [Accepted: 02/05/2024] [Indexed: 03/21/2024] Open
Abstract
Circular RNA (circRNA) is a common non-coding RNA and plays an important role in the diagnosis and therapy of human diseases, circRNA-disease associations prediction based on computational methods can provide a new way for better clinical diagnosis. In this article, we proposed a novel method for circRNA-disease associations prediction based on ensemble learning, named ELCDA. First, the association heterogeneous network was constructed via collecting multiple information of circRNAs and diseases, and multiple similarity measures are adopted here, then, we use metapath, matrix factorization and GraphSAGE-based models to extract features of nodes from different views, the final comprehensive features of circRNAs and diseases via ensemble learning, finally, a soft voting ensemble strategy is used to integrate the predicted results of all classifier. The performance of ELCDA is evaluated by fivefold cross-validation and compare with other state-of-the-art methods, the experimental results show that ELCDA is outperformance than others. Furthermore, three common diseases are used as case studies, which also demonstrate that ELCDA is an effective method for predicting circRNA-disease associations.
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Affiliation(s)
- Jing Yang
- School of Computer ScienceShaanxi Normal UniversityXi'anShaanxiChina
| | - Xiujuan Lei
- School of Computer ScienceShaanxi Normal UniversityXi'anShaanxiChina
| | - Fa Zhang
- School of Medical TechnologyBeijing Institute of TechnologyBeijingChina
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6
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Shi X, Liao S, Bi Z, Liu J, Li H, Feng C. Newly discovered circRNAs encoding proteins: recent progress. Front Genet 2023; 14:1264606. [PMID: 37829278 PMCID: PMC10565661 DOI: 10.3389/fgene.2023.1264606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023] Open
Abstract
Circular RNA (circRNA) is a special class of noncoding RNA molecules and the latest research hotspot in the field of RNA. CircRNA molecules have a closed loop structure, which is not affected by RNA exonuclease and has the characteristics of more stable expression. Previous studies have shown that circRNA molecules are rich in microRNA (miRNA) binding sites and act as miRNA sponges in cells. By interacting with miRNAs associated with tumors and other diseases, circRNAs play an important regulatory role. However, circRNAs have recently been found to have small open reading frames that enable them to encode peptides/proteins. These proteins have been reported to play an important role in the mechanism of regulation of a variety of diseases and have great potential in the diagnosis and treatment of diseases. In this review, we summarize the mechanism of action of the newly discovered circRNA-coding proteins since 2022 and briefly describe their research process. In addition, we also discuss the prediction model of the functional sites and encoded proteins of circRNAs, which provides a potential idea for future research on circRNAs.
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Affiliation(s)
- Xiaotong Shi
- Department of Obstetrics and Gynecology, Beijing Chao-yang Hospital of Capital Medical University, Beijing, China
- Department of Orthopedics, The First Hospital of Jilin University, Changchun, China
| | - Shiyu Liao
- Department of Orthopedics, The First Hospital of Jilin University, Changchun, China
| | - Zhiguo Bi
- Department of Orthopedics, The First Hospital of Jilin University, Changchun, China
| | - Jianguo Liu
- Department of Orthopedics, The First Hospital of Jilin University, Changchun, China
| | - Hua Li
- Department of Obstetrics and Gynecology, Beijing Chao-yang Hospital of Capital Medical University, Beijing, China
| | - Chunyang Feng
- Department of Obstetrics and Gynecology, Beijing Chao-yang Hospital of Capital Medical University, Beijing, China
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7
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Wu Q, Deng Z, Zhang W, Pan X, Choi KS, Zuo Y, Shen HB, Yu DJ. MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering. Bioinformatics 2023; 39:btad499. [PMID: 37561093 PMCID: PMC10457666 DOI: 10.1093/bioinformatics/btad499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/17/2023] [Accepted: 08/09/2023] [Indexed: 08/11/2023] Open
Abstract
MOTIVATION CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area of research. Due to the high cost of validating circRNA-disease associations using traditional wet-lab experiments, novel computational methods based on machine learning are gaining more and more attention in this field. However, current computational methods suffer to insufficient consideration of latent features in circRNA-disease interactions. RESULTS In this study, a multilayer attention neural graph-based collaborative filtering (MLNGCF) is proposed. MLNGCF first enhances multiple biological information with autoencoder as the initial features of circRNAs and diseases. Then, by constructing a central network of different diseases and circRNAs, a multilayer cooperative attention-based message propagation is performed on the central network to obtain the high-order features of circRNAs and diseases. A neural network-based collaborative filtering is constructed to predict the unknown circRNA-disease associations and update the model parameters. Experiments on the benchmark datasets demonstrate that MLNGCF outperforms state-of-the-art methods, and the prediction results are supported by the literature in the case studies. AVAILABILITY AND IMPLEMENTATION The source codes and benchmark datasets of MLNGCF are available at https://github.com/ABard0/MLNGCF.
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Affiliation(s)
- Qunzhuo Wu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Zhaohong Deng
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Wei Zhang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China
| | - Kup-Sze Choi
- The Centre for Smart Health, The Hong Kong Polytechnic University, Hong Kong
| | - Yun Zuo
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
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8
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Yuan L, Zhao J, Shen Z, Zhang Q, Geng Y, Zheng CH, Huang DS. iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction. PLoS Comput Biol 2023; 19:e1011344. [PMID: 37651321 PMCID: PMC10470932 DOI: 10.1371/journal.pcbi.1011344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 07/10/2023] [Indexed: 09/02/2023] Open
Abstract
Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease association and achieved impressive prediction performance. However, there are two main drawbacks to these methods. The first is these methods underutilize biometric information in the data. Second, the features extracted by these methods are not outstanding to represent association characteristics between circRNAs and diseases. In this study, we developed a novel deep learning model, named iCircDA-NEAE, to predict circRNA-disease associations. In particular, we use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity simultaneously for the first time, and extract hidden features based on accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE). Experimental results on the circR2Disease dataset show that iCircDA-NEAE outperforms other competing methods significantly. Besides, 16 of the top 20 circRNA-disease pairs with the highest prediction scores were validated by relevant literature. Furthermore, we observe that iCircDA-NEAE can effectively predict new potential circRNA-disease associations.
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Affiliation(s)
- Lin Yuan
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Jiawang Zhao
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Zhen Shen
- School of Computer and Software, Nanyang Institute of Technology, Nanyang, China
| | - Qinhu Zhang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China
| | - Yushui Geng
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
| | - De-Shuang Huang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China
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9
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Siracusa C, Vono N, Morano MB, Sabatino J, Leo I, Eyileten C, Cianflone E, Postula M, Torella D, De Rosa S. Clinical Application of Circular RNAs as Biomarkers in Acute Ischemic Stroke. J Pers Med 2023; 13:jpm13050839. [PMID: 37241009 DOI: 10.3390/jpm13050839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 05/28/2023] Open
Abstract
Despite the substantial improvement in diagnosis and treatment within the last decades, ischemic stroke still represents a challenge, responsible still for a high burden of morbidity and mortality. Among the unmet clinical needs are the difficulties in identifying those subjects with the greatest risk of developing a stroke, the challenges in obtaining a timely diagnosis, the prompt recognition of the different clinical forms of stroke, the assessment of the response to treatments and the prognostic assessment. All these issues might be improved with appropriate smart biomarkers that could better inform clinical management. The present article offers an overview of the potential role of circular RNAs as disease biomarkers in stroke. A systematic approach was adopted to gather all potentially relevant information in order to provide a panoramic view on this class of promising molecules.
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Affiliation(s)
- Chiara Siracusa
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | - Niccolò Vono
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | - Maria Benedetta Morano
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | - Jolanda Sabatino
- Department of Children and Woman's Health, University of Padua, 35121 Padua, Italy
| | - Isabella Leo
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy
| | - Ceren Eyileten
- Centre for Preclinical Research and Technology, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, 02-097 Warsaw, Poland
- Genomics Core Facility, Center of New Technologies, University of Warsaw, 00-927 Warsaw, Poland
| | - Eleonora Cianflone
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
| | - Marek Postula
- Centre for Preclinical Research and Technology, Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Daniele Torella
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy
| | - Salvatore De Rosa
- Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
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10
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Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins. Comput Biol Med 2022; 151:106268. [PMID: 36370585 DOI: 10.1016/j.compbiomed.2022.106268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/28/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase, prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of the conventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years, a variety of machine learning-based methods that have been used on a large scale for large-scale screening of DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparse representation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solve nonlinear classification problems. And L2,1-matrix norm is introduced to get sparse solution of model. To evaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRC achieves better performance in experimental results.
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11
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Li P, Tiwari P, Xu J, Qian Y, Ai C, Ding Y, Guo F. Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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12
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Chen Y, Wang J, Wang C, Liu M, Zou Q. Deep learning models for disease-associated circRNA prediction: a review. Brief Bioinform 2022; 23:6696465. [PMID: 36130259 DOI: 10.1093/bib/bbac364] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 12/14/2022] Open
Abstract
Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.
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Affiliation(s)
- Yaojia Chen
- College of Electronics and Information Engineering Guangdong Ocean University, Zhanjiang, China and the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiacheng Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Mingxin Liu
- College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China
| | - Quan Zou
- University of Electronic Science and Technology of China, China
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13
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Wu E, Fan X, Tang T, Li J, Wang J, Liu X, Zungar Z, Ren J, Wu C, Shen B. Biomarkers discovery for endometrial cancer: A graph convolutional sample network method. Comput Biol Med 2022; 150:106200. [PMID: 37859290 DOI: 10.1016/j.compbiomed.2022.106200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 09/20/2022] [Accepted: 10/09/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Endometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer. METHODS A novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction. RESULTS Putative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers. CONCLUSIONS A novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.
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Affiliation(s)
- Erman Wu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xuemeng Fan
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Tang
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China; Department of Computer Science and Information Technologies, Elviña Campus, University of A Coruña, A Coruña, Spain
| | - Jingjing Li
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Jiao Wang
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Xingyun Liu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Zayatta Zungar
- School of Medicine, University of New England, Armidale, NSW, 2351, Australia
| | - Jiaojiao Ren
- School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, China
| | - Cong Wu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
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14
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Wu Q, Deng Z, Pan X, Shen HB, Choi KS, Wang S, Wu J, Yu DJ. MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction. Brief Bioinform 2022; 23:6652197. [PMID: 35907779 DOI: 10.1093/bib/bbac289] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/19/2022] [Accepted: 06/26/2022] [Indexed: 11/12/2022] Open
Abstract
Circular RNA (circRNA) is closely involved in physiological and pathological processes of many diseases. Discovering the associations between circRNAs and diseases is of great significance. Due to the high-cost to verify the circRNA-disease associations by wet-lab experiments, computational approaches for predicting the associations become a promising research direction. In this paper, we propose a method, MDGF-MCEC, based on multi-view dual attention graph convolution network (GCN) with cooperative ensemble learning to predict circRNA-disease associations. First, MDGF-MCEC constructs two disease relation graphs and two circRNA relation graphs based on different similarities. Then, the relation graphs are fed into a multi-view GCN for representation learning. In order to learn high discriminative features, a dual-attention mechanism is introduced to adjust the contribution weights, at both channel level and spatial level, of different features. Based on the learned embedding features of diseases and circRNAs, nine different feature combinations between diseases and circRNAs are treated as new multi-view data. Finally, we construct a multi-view cooperative ensemble classifier to predict the associations between circRNAs and diseases. Experiments conducted on the CircR2Disease database demonstrate that the proposed MDGF-MCEC model achieves a high area under curve of 0.9744 and outperforms the state-of-the-art methods. Promising results are also obtained from experiments on the circ2Disease and circRNADisease databases. Furthermore, the predicted associated circRNAs for hepatocellular carcinoma and gastric cancer are supported by the literature. The code and dataset of this study are available at https://github.com/ABard0/MDGF-MCEC.
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Affiliation(s)
| | - Zhaohong Deng
- Jiangnan University, School of Artificial Intelligence and Computer Science, China
| | - Xiaoyong Pan
- Shanghai Jiao Tong University, Department of Automation, China
| | - Hong-Bin Shen
- Shanghai Jiao Tong University, Shanghai, China, Department of Automation, China
| | - Kup-Sze Choi
- Hong Kong Polytechnic University, School of Nursing, China
| | - Shitong Wang
- Jiangnan University, School of Artificial Intelligence and Computer Science, China
| | - Jing Wu
- Jiangnan University, State Key Laboratory of Food Science and Technology, China
| | - Dong-Jun Yu
- Nanjing University of Science and Technology, School of Computer Science and Engineering, China
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15
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Li G, Lin Y, Luo J, Xiao Q, Liang C. GGAECDA: predicting circRNA-disease associations using graph autoencoder based on graph representation learning. Comput Biol Chem 2022; 99:107722. [DOI: 10.1016/j.compbiolchem.2022.107722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 11/27/2022]
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