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Li YC, You ZH, Yu CQ, Wang L, Hu L, Hu PW, Qiao Y, Wang XF, Huang YA. DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information. Brief Funct Genomics 2024; 23:276-285. [PMID: 37539561 DOI: 10.1093/bfgp/elad030] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 05/25/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023] Open
Abstract
Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.
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Affiliation(s)
- Yue-Chao Li
- School of Information Engineering, Xijing University, Xi'an, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an, China
| | - Lei Wang
- Guangxi Academy of Sciences, Nanning, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Yan Qiao
- College of Agriculture and Forestry, Longdong University, Qingyang 745000, China
| | - Xin-Fei Wang
- School of Information Engineering, Xijing University, Xi'an, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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2
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Niu M, Wang C, Zhang Z, Zou Q. A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation. BMC Biol 2024; 22:24. [PMID: 38281919 PMCID: PMC10823650 DOI: 10.1186/s12915-024-01826-z] [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: 07/20/2023] [Accepted: 01/11/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA. RESULTS CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs. CONCLUSIONS This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.
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Affiliation(s)
- Mengting Niu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150000, Heilongjiang, China
| | - Zhanguo Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No. 4 Block 2 North Jianshe Road, Chengdu, 610054, China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
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Liyaqat T, Ahmad T, Saxena C. TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection. J Comput Aided Mol Des 2023; 37:573-584. [PMID: 37777631 DOI: 10.1007/s10822-023-00533-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/07/2023] [Indexed: 10/02/2023]
Abstract
Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.
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Affiliation(s)
- Tanya Liyaqat
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
| | - Tanvir Ahmad
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
| | - Chandni Saxena
- The Chinese University of Hong Kong, Sha Tin, SAR, China
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4
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Su R, Zhuang J, Liu S, Liu D, Feng K. EnILs: A General Ensemble Computational Approach for Predicting Inducing Peptides of Multiple Interleukins. J Comput Biol 2023; 30:1289-1304. [PMID: 38010531 DOI: 10.1089/cmb.2023.0002] [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] [Indexed: 11/29/2023] Open
Abstract
Interleukins (ILs) are a group of multifunctional cytokines, which play important roles in immune regulations and inflammatory responses. Recently, IL-6 has been found to affect the development of COVID-19, and significantly elevated levels of IL-6 cytokines have been reported in patients with severe COVID-19. IL-10 and IL-17 are anti-inflammatory and proinflammatory cytokines, respectively, which play multiple protective roles in host defense against pathogens. At present, a number of machine learning methods have been proposed to predict ILs inducing peptides, but their predictive performance needs to be further improved, and the inducing peptides of different ILs are predicted separately, rather than using a general approach. In our work, we combine the statistical features of peptide sequence with word embedding to design a general ensemble model named EnILs to predict inducing peptides of different ILs, in which the predictive probabilities of random forest, eXtreme Gradient Boosting and neural network are integrated in an average way. Compared with the state-of-the-art machine learning methods, EnILs shows considerable performance in the prediction of IL-6, IL-10, and IL-17 inducing peptides. In addition, we predict the most promising IL-6 inducing peptides in Severe Acute Respiratory Syndrome Coronavirus 2 spike protein in the case study for further experimental verification.
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Affiliation(s)
- Rui Su
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Jujuan Zhuang
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Shuhan Liu
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
| | - Di Liu
- Department of Computer Science and Technology, Information Science and Technology College, Dalian Maritime University, Dalian, Liaoning, China
| | - Kexin Feng
- Department of Statistics, School of Science, Dalian Maritime University, Dalian, Liaoning, China
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Shi M, Li X, Li M, Si Y. Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data. Brief Bioinform 2023; 24:bbad329. [PMID: 37756592 DOI: 10.1093/bib/bbad329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 08/20/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.
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Affiliation(s)
- Mingguang Shi
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Xuefeng Li
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Mingna Li
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China
| | - Yichong Si
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China
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6
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Ma Z, Kuang Z, Deng L. NGCICM: A Novel Deep Learning-Based Method for Predicting circRNA-miRNA Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3080-3092. [PMID: 37027645 DOI: 10.1109/tcbb.2023.3248787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The circRNAs and miRNAs play an important role in the development of human diseases, and they can be widely used as biomarkers of diseases for disease diagnosis. In particular, circRNAs can act as sponge adsorbers for miRNAs and act together in certain diseases. However, the associations between the vast majority of circRNAs and diseases and between miRNAs and diseases remain unclear. Computational-based approaches are urgently needed to discover the unknown interactions between circRNAs and miRNAs. In this paper, we propose a novel deep learning algorithm based on Node2vec and Graph ATtention network (GAT), Conditional Random Field (CRF) layer and Inductive Matrix Completion (IMC) to predict circRNAs and miRNAs interactions (NGCICM). We construct a GAT-based encoder for deep feature learning by fusing the talking-heads attention mechanism and the CRF layer. The IMC-based decoder is also constructed to obtain interaction scores. The Area Under the receiver operating characteristic Curve (AUC) of the NGCICM method is 0.9697, 0.9932 and 0.9980, and the Area Under the Precision-Recall curve (AUPR) is 0.9671, 0.9935 and 0.9981, respectively, using 2-fold, 5-fold and 10-fold Cross-Validation (CV) as the benchmark. The experimental results confirm the effectiveness of the NGCICM algorithm in predicting the interactions between circRNAs and miRNAs.
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7
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Li F, Li PF, Hao XD. Circular RNAs in ferroptosis: regulation mechanism and potential clinical application in disease. Front Pharmacol 2023; 14:1173040. [PMID: 37332354 PMCID: PMC10272566 DOI: 10.3389/fphar.2023.1173040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 05/25/2023] [Indexed: 06/20/2023] Open
Abstract
Ferroptosis, an iron-dependent non-apoptotic form of cell death, is reportedly involved in the pathogenesis of various diseases, particularly tumors, organ injury, and degenerative pathologies. Several signaling molecules and pathways have been found to be involved in the regulation of ferroptosis, including polyunsaturated fatty acid peroxidation, glutathione/glutathione peroxidase 4, the cysteine/glutamate antiporter system Xc-, ferroptosis suppressor protein 1/ubiquinone, and iron metabolism. An increasing amount of evidence suggests that circular RNAs (circRNAs), which have a stable circular structure, play important regulatory roles in the ferroptosis pathways that contribute to disease progression. Hence, ferroptosis-inhibiting and ferroptosis-stimulating circRNAs have potential as novel diagnostic markers or therapeutic targets for cancers, infarctions, organ injuries, and diabetes complications linked to ferroptosis. In this review, we summarize the roles that circRNAs play in the molecular mechanisms and regulatory networks of ferroptosis and their potential clinical applications in ferroptosis-related diseases. This review furthers our understanding of the roles of ferroptosis-related circRNAs and provides new perspectives on ferroptosis regulation and new directions for the diagnosis, treatment, and prognosis of ferroptosis-related diseases.
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8
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Wu P, Nie Z, Huang Z, Zhang X. CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model. PLANTS (BASEL, SWITZERLAND) 2023; 12:1652. [PMID: 37111874 PMCID: PMC10143888 DOI: 10.3390/plants12081652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/10/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 - 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge.
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Affiliation(s)
- Pengpeng Wu
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Life Science, Anhui Agricultural University, Hefei 230036, China
| | - Zhenjun Nie
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
| | - Zhiqiang Huang
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
| | - Xiaodan Zhang
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
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9
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Tyagi N, Bhushan B. Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. WIRELESS PERSONAL COMMUNICATIONS 2023; 130:857-908. [PMID: 37168438 PMCID: PMC10019426 DOI: 10.1007/s11277-023-10312-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/25/2023] [Indexed: 05/13/2023]
Abstract
Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.
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Affiliation(s)
- Nemika Tyagi
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
| | - Bharat Bhushan
- Department of Computer Science and Engineering School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310 India
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10
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Lu C, Zhang L, Zeng M, Lan W, Duan G, Wang J. Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network. Brief Bioinform 2023; 24:6960978. [PMID: 36572658 DOI: 10.1093/bib/bbac549] [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: 08/19/2022] [Revised: 11/03/2022] [Accepted: 11/11/2022] [Indexed: 12/28/2022] Open
Abstract
Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.
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Affiliation(s)
- Chengqian Lu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China.,School of Computer Science, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Lishen Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
| | - Wei Lan
- School of Computer, Electronic and Information, Guangxi University, Nanning, 530004, Guangxi, China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
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11
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Zheng K, Zhang XL, Wang L, You ZH, Ji BY, Liang X, Li ZW. SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs. Brief Bioinform 2023; 24:6850564. [PMID: 36445194 DOI: 10.1093/bib/bbac498] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 11/30/2022] Open
Abstract
piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.
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Affiliation(s)
- Kai Zheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China.,College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China
| | - Xin-Lu Zhang
- Civil Product General Research Institute, The 36th Research Institute of China Electronics Technology Group Corporation, Jiaxing, 314000, China
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 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
| | - Bo-Ya Ji
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410006, China
| | - Xiao Liang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Zheng-Wei Li
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.,Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China
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12
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Peng L, Yang J, Wang M, Zhou L. Editorial: Machine learning-based methods for RNA data analysis—Volume II. Front Genet 2022; 13:1010089. [DOI: 10.3389/fgene.2022.1010089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/20/2022] [Indexed: 12/02/2022] Open
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13
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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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14
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Li Y, Hu XG, Wang L, Li PP, You ZH. MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances. Brief Bioinform 2022; 23:6831006. [PMID: 36384071 DOI: 10.1093/bib/bbac479] [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: 07/11/2022] [Revised: 09/25/2022] [Accepted: 10/10/2022] [Indexed: 11/18/2022] Open
Abstract
Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease associations, and uncovering new circRNA-disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases. Firstly, we computed different biological attribute information of circRNA and disease separately and used them to construct multiple multi-source similarity networks. Then, we used the high-order GCN algorithm to learn feature embedding representations with high-order mixed neighborhood information of circRNA and disease from the constructed multi-source similarity networks, respectively. Finally, the deep neural network classifier was implemented to predict associations of circRNAs with diseases. The MNMDCDA model obtained AUC scores of 95.16%, 94.53%, 89.80% and 91.83% on four benchmark datasets, i.e., CircR2Disease, CircAtlas v2.0, Circ2Disease and CircRNADisease, respectively, using the 5-fold cross-validation approach. Furthermore, 25 of the top 30 circRNA-disease pairs with the best scores of MNMDCDA in the case study were validated by recent literature. Numerous experimental results indicate that MNMDCDA can be used as an effective computational tool to predict circRNA-disease associations and can provide the most promising candidates for biological experiments.
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Affiliation(s)
- Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
| | - Xue-Gang Hu
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
| | - Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China.,College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China
| | - Pei-Pei Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China.,School of Computer Science, Northwestern Polytechnical University, Xi'an Shaanxi 710129, China
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15
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Su Q, Tan Q, Liu X, Wu L. Prioritizing potential circRNA biomarkers for bladder cancer and bladder urothelial cancer based on an ensemble model. Front Genet 2022; 13:1001608. [PMID: 36186429 PMCID: PMC9521272 DOI: 10.3389/fgene.2022.1001608] [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: 07/23/2022] [Accepted: 08/15/2022] [Indexed: 12/03/2022] Open
Abstract
Bladder cancer is the most common cancer of the urinary system. Bladder urothelial cancer accounts for 90% of bladder cancer. These two cancers have high morbidity and mortality rates worldwide. The identification of biomarkers for bladder cancer and bladder urothelial cancer helps in their diagnosis and treatment. circRNAs are considered oncogenes or tumor suppressors in cancers, and they play important roles in the occurrence and development of cancers. In this manuscript, we developed an Ensemble model, CDA-EnRWLRLS, to predict circRNA-Disease Associations (CDA) combining Random Walk with restart and Laplacian Regularized Least Squares, and further screen potential biomarkers for bladder cancer and bladder urothelial cancer. First, we compute disease similarity by combining the semantic similarity and association profile similarity of diseases and circRNA similarity by combining the functional similarity and association profile similarity of circRNAs. Second, we score each circRNA-disease pair by random walk with restart and Laplacian regularized least squares, respectively. Third, circRNA-disease association scores from these models are integrated to obtain the final CDAs by the soft voting approach. Finally, we use CDA-EnRWLRLS to screen potential circRNA biomarkers for bladder cancer and bladder urothelial cancer. CDA-EnRWLRLS is compared to three classical CDA prediction methods (CD-LNLP, DWNN-RLS, and KATZHCDA) and two individual models (CDA-RWR and CDA-LRLS), and obtains better AUC of 0.8654. We predict that circHIPK3 has the highest association with bladder cancer and may be its potential biomarker. In addition, circSMARCA5 has the highest association with bladder urothelial cancer and may be its possible biomarker.
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16
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Wang L, Wong L, Li Z, Huang Y, Su X, Zhao B, You Z. A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction. Brief Bioinform 2022; 23:6693603. [PMID: 36070867 DOI: 10.1093/bib/bbac388] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/26/2022] [Accepted: 08/11/2022] [Indexed: 11/14/2022] Open
Abstract
Circular RNAs (circRNAs) are involved in the regulatory mechanisms of multiple complex diseases, and the identification of their associations is critical to the diagnosis and treatment of diseases. In recent years, many computational methods have been designed to predict circRNA-disease associations. However, most of the existing methods rely on single correlation data. Here, we propose a machine learning framework for circRNA-disease association prediction, called MLCDA, which effectively fuses multiple sources of heterogeneous information including circRNA sequences and disease ontology. Comprehensive evaluation in the gold standard dataset showed that MLCDA can successfully capture the complex relationships between circRNAs and diseases and accurately predict their potential associations. In addition, the results of case studies on real data show that MLCDA significantly outperforms other existing methods. MLCDA can serve as a useful tool for circRNA-disease association prediction, providing mechanistic insights for disease research and thus facilitating the progress of disease treatment.
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Affiliation(s)
- Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Zhengwei Li
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Yuan Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Xiaorui Su
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Bowei Zhao
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
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17
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Li N, Wang Y, Wu X. Knockdown of Circ_0037658 Alleviates IL-1β-Induced Osteoarthritis Progression by Serving as a Sponge of miR-665 to Regulate ADAMTS5. Front Genet 2022; 13:886898. [PMID: 36092909 PMCID: PMC9449488 DOI: 10.3389/fgene.2022.886898] [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: 03/01/2022] [Accepted: 06/17/2022] [Indexed: 11/21/2022] Open
Abstract
Background: Osteoarthritis (OA) is a chronic musculoskeletal degeneration disease which brings great pain to patients and a tremendous burden on the world’s medical resources. Previous reports have indicated that circular RNAs (circRNAs) are involved in the pathogenesis of OA. The purpose of this study was to explore the role and mechanism of circ_0037658 in the OA cell model. Methods: The content of interleukin-6 (IL-6) and tumor necrosis factor α (TNF-α) was measured using enzyme-linked immunosorbent assay (ELISA). Cell proliferation ability and apoptosis were detected using Cell Counting Kit-8 (CCK-8), 5-ethynyl-2′-deoxyuridine (EDU), and flow cytometry assays. Western blot assay was used to measure the protein levels of Bcl-2-related X protein (Bax), cleaved-caspase-3, MMP13, Aggrecan, and ADAMTS5. The expression of circ_0037658, microRNA-665 (miR-665), and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) 5 was detected using real-time quantitative polymerase chain reaction (RT-qPCR). Dual-luciferase reporter assay and RNA Immunoprecipitation (RIP) assay were manipulated to analyze the relationships of circ_0037658, miR-665, and ADAMTS5. Results: Human chondrocytes (CHON-001 cells) were treated with interleukin-1β (IL-1β) to establish an OA cell model. Circ_0037658 and ADAMTS5 levels were increased, and miR-665 was decreased in OA cartilage samples and IL-1β-treated chondrocyte cells. Moreover, circ_0037658 silencing promoted proliferation and impaired inflammation, apoptosis, and ECM degradation in IL-1β-treated CHON-001 cells. Mechanically, circ_0037658 acted as a sponge for miR-665 to regulate ADAMTS5 expression. Conclusion: Circ_0037658 knockdown relieved IL-1β-triggered chondrocyte injury via regulating the miR-665/ADAMTS5 axis, promising an underlying therapeutic strategy for OA.
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Affiliation(s)
- Ningbo Li
- Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Orthopedics, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Yongsheng Wang
- Department of Orthopedics, The First Affiliated Hospital of Henan University, Kaifeng, China
| | - Xuejian Wu
- Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Xuejian Wu,
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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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19
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Yao X, Zhang Q. Function and Clinical Significance of Circular RNAs in Thyroid Cancer. Front Mol Biosci 2022; 9:925389. [PMID: 35936780 PMCID: PMC9353217 DOI: 10.3389/fmolb.2022.925389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/22/2022] [Indexed: 12/28/2022] Open
Abstract
Thyroid cancer (TC) is the leading cause and mortality of endocrine malignancies worldwide. Tumourigenesis involves multiple molecules including circular RNAs (circRNAs). circRNAs with covalently closed single-stranded structures have been identified as a type of regulatory RNA because of their high stability, abundance, and tissue/developmental stage-specific expression. Accumulating evidence has demonstrated that various circRNAs are aberrantly expressed in thyroid tissues, cells, exosomes, and body fluids in patients with TC. CircRNAs have been identified as either oncogenic or tumour suppressor roles in regulating tumourigenesis, tumour metabolism, metastasis, ferroptosis, and chemoradiation resistance in TC. Importantly, circRNAs exert pivotal effects on TC through various mechanisms, including acting as miRNA sponges or decoys, interacting with RNA-binding proteins, and translating functional peptides. Recent studies have suggested that many different circRNAs are associated with certain clinicopathological features, implying that the altered expression of circRNAs may be characteristic of TC. The purpose of this review is to provide an overview of recent advances on the dysregulation, functions, molecular mechanisms and potential clinical applications of circRNAs in TC. This review also aimes to improve our understanding of the functions of circRNAs in the initiation and progression of cancer, and to discuss the future perspectives on strategies targeting circRNAs in TC.
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20
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Wang Y, Wang L, Wong L, Zhao B, Su X, Li Y, You Z. RoFDT: Identification of Drug–Target Interactions from Protein Sequence and Drug Molecular Structure Using Rotation Forest. BIOLOGY 2022; 11:biology11050741. [PMID: 35625469 PMCID: PMC9138819 DOI: 10.3390/biology11050741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
As the basis for screening drug candidates, the identification of drug–target interactions (DTIs) plays a crucial role in the innovative drugs research. However, due to the inherent constraints of small-scale and time-consuming wet experiments, DTI recognition is usually difficult to carry out. In the present study, we developed a computational approach called RoFDT to predict DTIs by combining feature-weighted Rotation Forest (FwRF) with a protein sequence. In particular, we first encode protein sequences as numerical matrices by Position-Specific Score Matrix (PSSM), then extract their features utilize Pseudo Position-Specific Score Matrix (PsePSSM) and combine them with drug structure information-molecular fingerprints and finally feed them into the FwRF classifier and validate the performance of RoFDT on Enzyme, GPCR, Ion Channel and Nuclear Receptor datasets. In the above dataset, RoFDT achieved 91.68%, 84.72%, 88.11% and 78.33% accuracy, respectively. RoFDT shows excellent performance in comparison with support vector machine models and previous superior approaches. Furthermore, 7 of the top 10 DTIs with RoFDT estimate scores were proven by the relevant database. These results demonstrate that RoFDT can be employed to a powerful predictive approach for DTIs to provide theoretical support for innovative drug discovery.
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Affiliation(s)
- Ying Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, 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;
- Correspondence: (L.W.); (Z.Y.); Tel.: +86-151-0632-2257 (L.W.); +86-173-9276-3836 (Z.Y.)
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
| | - Bowei Zhao
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; (B.Z.); (X.S.)
| | - Xiaorui Su
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; (B.Z.); (X.S.)
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China;
| | - Zhuhong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
- Correspondence: (L.W.); (Z.Y.); Tel.: +86-151-0632-2257 (L.W.); +86-173-9276-3836 (Z.Y.)
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21
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Wang L, Wong L, Chen ZH, Hu J, Sun XF, Li Y, You ZH. MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information. BIOLOGY 2022; 11:740. [PMID: 35625468 PMCID: PMC9138588 DOI: 10.3390/biology11050740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 11/25/2022]
Abstract
The key to new drug discovery and development is first and foremost the search for molecular targets of drugs, thus advancing drug discovery and drug repositioning. However, traditional drug-target interactions (DTIs) is a costly, lengthy, high-risk, and low-success-rate system project. Therefore, more and more pharmaceutical companies are trying to use computational technologies to screen existing drug molecules and mine new drugs, leading to accelerating new drug development. In the current study, we designed a deep learning computational model MSPEDTI based on Molecular Structure and Protein Evolutionary to predict the potential DTIs. The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). In cross-validation experiments, MSPEDTI achieved 94.19%, 90.95%, 87.95%, and 86.11% prediction accuracy in the gold-standard datasets enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors, respectively. MSPEDTI showed its competitive ability in ablation experiments and comparison with previous excellent methods. Additionally, 7 of 10 potential DTIs predicted by MSPEDTI were substantiated by the classical database. These excellent outcomes demonstrate the ability of MSPEDTI to provide reliable drug candidate targets and strongly facilitate the development of drug repositioning and drug development.
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Affiliation(s)
- Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
| | - Zhan-Heng Chen
- Computer Science and Technology, Tongji University, Shanghai 200092, China;
| | - Jing Hu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Xiao-Fei Sun
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China;
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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22
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Zhou J, He S, Wang B, Yang W, Zheng Y, Jiang S, Li D, Lin J. Construction and Bioinformatics Analysis of circRNA-miRNA-mRNA Network in Acute Myocardial Infarction. Front Genet 2022; 13:854993. [PMID: 35422846 PMCID: PMC9002054 DOI: 10.3389/fgene.2022.854993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/02/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Acute myocardial infarction (AMI) is one of the main fatal diseases of cardiovascular diseases. Circular RNA (circRNA) is a non-coding RNA (ncRNA), which plays a role in cardiovascular disease as a competitive endogenous RNA (ceRNA). However, their role in AMI has not been fully clarified. This study aims to explore the mechanism of circRNA-related ceRNA network in AMI, and to identify the corresponding immune infiltration characteristics. Materials and Methods: The circRNA (GSE160717), miRNA (GSE24548), and mRNA (GSE60993) microarray datasets of AMI were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified by the “limma” package. After integrating the circRNA, miRNA and mRNA interaction, we constructed a circRNA-miRNA-mRNA network. The “clusterProfiler” package and String database were used for functional enrichment analysis and protein-protein interaction (PPI) analysis, respectively. After that, we constructed a circRNA-miRNA-hub gene network and validated the circRNAs and mRNAs using an independent dataset (GSE61144) as well as qRT-PCR. Finally, we used CIBERSORTx database to analyze the immune infiltration characteristics of AMI and the correlation between hub genes and immune cells. Results: Using the “limma” package of the R, 83 DEcircRNAs, 54 DEmiRNAs, and 754 DEmRNAs were identified in the microarray datasets of AMI. Among 83 DEcircRNAs, there are 55 exonic DEcircRNAs. Then, a circRNA-miRNA-mRNA network consists of 21 DEcircRNAs, 11 DEmiRNAs, and 106 DEmRNAs were predicted by the database. After that, 10 hub genes from the PPI network were identified. Then, a new circRNA-miRNA-hub gene network consists of 14 DEcircRNAs, 7 DEmiRNAs, and 9 DEmRNAs was constructed. After that, three key circRNAs (hsa_circ_0009018, hsa_circ_0030569 and hsa_circ_0031017) and three hub genes (BCL6, PTGS2 and PTEN) were identified from the network by qRT-PCR. Finally, immune infiltration analysis showed that hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. Conclusion: Our study constructed a circRNA-related ceRNA networks in AMI, consists of hsa_circ_0031017/hsa-miR-142-5p/PTEN axis, hsa_circ_0030569/hsa-miR-545/PTGS2 axis and hsa_circ_0009018/hsa-miR-139-3p/BCL6 axis. These three hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. It helps improve understanding of AMI mechanism and provides future potential therapeutic targets.
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Affiliation(s)
- Jin Zhou
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shaolin He
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Boyuan Wang
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenling Yang
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuqi Zheng
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shijiu Jiang
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dazhu Li
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jibin Lin
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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23
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Bi XA, Li L, Wang Z, Wang Y, Luo X, Xu L. IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data. Brief Bioinform 2022; 23:6554128. [PMID: 35348583 DOI: 10.1093/bib/bbac093] [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: 12/18/2021] [Revised: 01/28/2022] [Accepted: 02/23/2022] [Indexed: 11/13/2022] Open
Abstract
Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.
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Affiliation(s)
- Xia-An Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Lou Li
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zizheng Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yu Wang
- Department of Computing, School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xun Luo
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, Changsha 410081, P.R. China
| | - Luyun Xu
- College of Business, Hunan Normal University, Changsha 410081, P.R. China
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24
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Zhang HY, Wang L, You ZH, Hu L, Zhao BW, Li ZW, Li YM. iGRLCDA: identifying circRNA-disease association based on graph representation learning. Brief Bioinform 2022; 23:6552271. [PMID: 35323894 DOI: 10.1093/bib/bbac083] [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: 01/19/2022] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 12/18/2022] Open
Abstract
While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) and graph factorization (GF). In detail, iGRLCDA first derived the hidden feature of known associations between circRNA and disease using the Gaussian interaction profile (GIP) kernel combined with disease semantic information to form a numeric descriptor. After that, it further used the deep learning model of GCN and GF to extract hidden features from the descriptor. Finally, the random forest classifier is introduced to identify the potential circRNA-disease association. The five-fold cross-validation of iGRLCDA shows strong competitiveness in comparison with other excellent prediction models at the gold standard data and achieved an average area under the receiver operating characteristic curve of 0.9289 and an area under the precision-recall curve of 0.9377. On reviewing the prediction results from the relevant literature, 22 of the top 30 predicted circRNA-disease associations were noted in recent published papers. These exceptional results make us believe that iGRLCDA can provide reliable circRNA-disease associations for medical research and reduce the blindness of wet-lab experiments.
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Affiliation(s)
- Han-Yuan Zhang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China.,College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Bo-Wei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Zheng-Wei Li
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
| | - Yang-Ming Li
- College of Engineering Technology, Rochester Institute of Technology, Rochester, NY 14623, USA
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25
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Yang Y, Hou Z, Wang Y, Ma H, Sun P, Ma Z, Wong KC, Li X. HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network. Brief Bioinform 2022; 23:6533504. [PMID: 35189638 DOI: 10.1093/bib/bbac027] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/03/2022] [Accepted: 01/17/2022] [Indexed: 01/11/2023] Open
Abstract
Identifying genome-wide binding events between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) can greatly facilitate our understanding of functional mechanisms within circRNAs. Thanks to the development of cross-linked immunoprecipitation sequencing technology, large amounts of genome-wide circRNA binding event data have accumulated, providing opportunities for designing high-performance computational models to discriminate RBP interaction sites and thus to interpret the biological significance of circRNAs. Unfortunately, there are still no computational models sufficiently flexible to accommodate circRNAs from different data scales and with various degrees of feature representation. Here, we present HCRNet, a novel end-to-end framework for identification of circRNA-RBP binding events. To capture the hierarchical relationships, the multi-source biological information is fused to represent circRNAs, including various natural language sequence features. Furthermore, a deep temporal convolutional network incorporating global expectation pooling was developed to exploit the latent nucleotide dependencies in an exhaustive manner. We benchmarked HCRNet on 37 circRNA datasets and 31 linear RNA datasets to demonstrate the effectiveness of our proposed method. To evaluate further the model's robustness, we performed HCRNet on a full-length dataset containing 740 circRNAs. Results indicate that HCRNet generally outperforms existing methods. In addition, motif analyses were conducted to exhibit the interpretability of HCRNet on circRNAs. All supporting source code and data can be downloaded from https://github.com/yangyn533/HCRNet and https://doi.org/10.6084/m9.figshare.16943722.v1. And the web server of HCRNet is publicly accessible at http://39.104.118.143:5001/.
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Affiliation(s)
- Yuning Yang
- School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China
| | - Zilong Hou
- School of Artificial Intelligence, Jilin University, Changchun, Jilin, China
| | - Yansong Wang
- School of Artificial Intelligence, Jilin University, Changchun, Jilin, China
| | - Hongli Ma
- School of Mathematics, Shandong University, Jinan, Shandong, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China
| | - Ka-Chun Wong
- School of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Changchun, Jilin, China
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26
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Ma Z, Kuang Z, Deng L. CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network. BMC Bioinformatics 2021; 22:551. [PMID: 34772332 PMCID: PMC8588735 DOI: 10.1186/s12859-021-04467-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. RESULTS In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. CONCLUSIONS After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.
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Affiliation(s)
- Zhihao Ma
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Zhufang Kuang
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
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