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Qiao LJ, Gao Z, Ji CM, Liu ZH, Zheng CH, Wang YT. Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3154-3162. [PMID: 37018084 DOI: 10.1109/tcbb.2023.3264466] [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
Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.
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2
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Ding P, Zeng M, Yin R. Editorial: Computational methods to analyze RNA data for human diseases. Front Genet 2023; 14:1270334. [PMID: 37674479 PMCID: PMC10478215 DOI: 10.3389/fgene.2023.1270334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/14/2023] [Indexed: 09/08/2023] Open
Affiliation(s)
- Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Rui Yin
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
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3
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DRGCNCDA: Predicting circRNA-disease interactions based on knowledge graph and disentangled relational graph convolutional network. Methods 2022; 208:35-41. [DOI: 10.1016/j.ymeth.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/15/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
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4
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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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5
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Deng L, Liu D, Li Y, Wang R, Liu J, Zhang J, Liu H. MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network. BMC Bioinformatics 2022; 23:427. [PMID: 36241972 PMCID: PMC9569055 DOI: 10.1186/s12859-022-04976-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 09/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. RESULTS In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. CONCLUSIONS Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Dayun Liu
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Yizhan Li
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Runqi Wang
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Junyi Liu
- Viterbi School of Engineering, University of Southern California, Los Angeles, 90089, USA
| | - Jiaxuan Zhang
- Department of Cognitive Science, University of California San Diego, La Jolla, 92093, USA
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816, China.
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6
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Niu M, Zou Q, Wang C. GMNN2CD: identification of circRNA-disease associations based on variational inference and graph Markov neural networks. Bioinformatics 2022; 38:2246-2253. [PMID: 35157027 DOI: 10.1093/bioinformatics/btac079] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/05/2021] [Accepted: 02/09/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for searching the etiopathogenesis and treatment of diseases. Nevertheless, it is inefficient to learn new associations only through biotechnology. RESULTS Consequently, we present a computational method, GMNN2CD, which employs a graph Markov neural network (GMNN) algorithm to predict unknown circRNA-disease associations. First, used verified associations, we calculate semantic similarity and Gaussian interactive profile kernel similarity (GIPs) of the disease and the GIPs of circRNA and then merge them to form a unified descriptor. After that, GMNN2CD uses a fusion feature variational map autoencoder to learn deep features and uses a label propagation map autoencoder to propagate tags based on known associations. Based on variational inference, GMNN alternate training enhances the ability of GMNN2CD to obtain high-efficiency high-dimensional features from low-dimensional representations. Finally, 5-fold cross-validation of five benchmark datasets shows that GMNN2CD is superior to the state-of-the-art methods. Furthermore, case studies have shown that GMNN2CD can detect potential associations. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/nmt315320/GMNN2CD.git.
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Affiliation(s)
- Mengting Niu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150000, China
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7
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Wang CC, Han CD, Zhao Q, Chen X. Circular RNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2021; 22:bbab286. [PMID: 34329377 PMCID: PMC8575014 DOI: 10.1093/bib/bbab286] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/23/2021] [Accepted: 07/03/2021] [Indexed: 12/13/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.
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Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology
| | - Chen-Di Han
- School of Information and Control Engineering, China University of Mining and Technology
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning
| | - Xing Chen
- China University of Mining and Technology
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Xiao Q, Dai J, Luo J. A survey of circular RNAs in complex diseases: databases, tools and computational methods. Brief Bioinform 2021; 23:6407737. [PMID: 34676391 DOI: 10.1093/bib/bbab444] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 01/22/2023] Open
Abstract
Circular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.
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Affiliation(s)
- Qiu Xiao
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
| | - Jianhua Dai
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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Xie G, Chen H, Sun Y, Gu G, Lin Z, Wang W, Li J. Predicting circRNA-Disease Associations Based on Deep Matrix Factorization with Multi-source Fusion. Interdiscip Sci 2021; 13:582-594. [PMID: 34185304 DOI: 10.1007/s12539-021-00455-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/18/2021] [Accepted: 06/20/2021] [Indexed: 12/14/2022]
Abstract
Recently, circRNAs with covalently closed loops have been discovered to play important parts in the progression of diseases. Nevertheless, the study of circRNA-disease associations is highly dependent on biological experiments, which are time-consuming and expensive. Hence, a computational approach to predict circRNA-disease associations is urgently needed. In this paper, we presented an approach that is based on deep matrix factorization with multi-source fusion (DMFMSF). In DMFMSF, several useful circRNA and disease similarities were selected and then combined by similarity kernel fusion. Then, linear and non-linear characteristics were mined using singular value decomposition (SVD) and deep matrix factorization to infer potential circRNA-disease associations. Performance of the proposed DMFMSF on two benchmark datasets are rigorously validated by leave-one-out cross-validation(LOOCV) and fivefold cross-validation (5-fold CV). The experimental results showed that DMFMSF is superior over several existing computational approaches. In addition, five important diseases, hepatocellular carcinoma, breast cancer, acute myeloid leukemia, colorectal cancer, and coronary artery disease were applied in case studies. The results suggest that DMFMSF can be used as an accurate and efficient computational tool for predicting circRNA-disease associations.
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Affiliation(s)
- Guobo Xie
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Hui Chen
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Yuping Sun
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Guosheng Gu
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Zhiyi Lin
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Weiming Wang
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.,School of Science and Technology, The Open University of Hong Kong, Hong Kong, 999077, China
| | - Jianming Li
- School of Computers, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
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10
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Zuo ZL, Cao RF, Wei PJ, Xia JF, Zheng CH. Double matrix completion for circRNA-disease association prediction. BMC Bioinformatics 2021; 22:307. [PMID: 34103016 PMCID: PMC8185931 DOI: 10.1186/s12859-021-04231-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/28/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient. RESULTS In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model. CONCLUSION The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.
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Affiliation(s)
- Zong-Lan Zuo
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China
| | - Rui-Fen Cao
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China
| | - Pi-Jing Wei
- Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Jun-Feng Xia
- Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China.
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Xiao Q, Fu Y, Yang Y, Dai J, Luo J. NSL2CD: identifying potential circRNA-disease associations based on network embedding and subspace learning. Brief Bioinform 2021; 22:6265177. [PMID: 33954582 DOI: 10.1093/bib/bbab177] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 03/29/2021] [Accepted: 04/14/2021] [Indexed: 12/28/2022] Open
Abstract
Many studies have evidenced that circular RNAs (circRNAs) are important regulators in various pathological processes and play vital roles in many human diseases, which could serve as promising biomarkers for disease diagnosis, treatment and prognosis. However, the functions of most of circRNAs remain to be unraveled, and it is time-consuming and costly to uncover those relationships between circRNAs and diseases by conventional experimental methods. Thus, identifying candidate circRNAs for human diseases offers new opportunities to understand the functional properties of circRNAs and the pathogenesis of diseases. In this study, we propose a novel network embedding-based adaptive subspace learning method (NSL2CD) for predicting potential circRNA-disease associations and discovering those disease-related circRNA candidates. The proposed method first calculates disease similarities and circRNA similarities by fully utilizing different data sources and learns low-dimensional node representations with network embedding methods. Then, we adopt an adaptive subspace learning model to discover potential associations between circRNAs and diseases. Meanwhile, an integrated weighted graph regularization term is imposed to preserve local geometric structures of data spaces, and L1,2-norm constraint is also incorporated into the model to realize the smoothness and sparsity of projection matrices. The experiment results show that NSL2CD achieves comparable performance under different evaluation metrics, and case studies further confirm its ability to discover potential candidate circRNAs for human diseases.
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Affiliation(s)
- Qiu Xiao
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, China
| | - Yu Fu
- Hunan Normal University, China
| | - Yide Yang
- School of Medicine, Hunan Normal University, China
| | - Jianhua Dai
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, China
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12
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Lei X, Mudiyanselage TB, Zhang Y, Bian C, Lan W, Yu N, Pan Y. A comprehensive survey on computational methods of non-coding RNA and disease association prediction. Brief Bioinform 2020; 22:6042241. [PMID: 33341893 DOI: 10.1093/bib/bbaa350] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/20/2020] [Accepted: 11/01/2020] [Indexed: 02/06/2023] Open
Abstract
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.
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Affiliation(s)
- Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | | | - Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Wei Lan
- School of Computer, Electronics and Information at Guangxi University, Nanning, China
| | - Ning Yu
- Department of Computing Sciences at the College at Brockport, State University of New York, Rochester, NY, USA
| | - Yi Pan
- Computer Science Department at Georgia State University, Atlanta, GA, USA
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13
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Xiao Q, Zhong J, Tang X, Luo J. iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion. Mol Genet Genomics 2020; 296:223-233. [PMID: 33159254 DOI: 10.1007/s00438-020-01741-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/23/2020] [Indexed: 01/22/2023]
Abstract
Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately. However, it is tremendously laborious and time-consuming to discover disease-related circRNAs with conventional biological experiments. In this study, we develop an integrative framework, called iCDA-CMG, to predict potential associations between circRNAs and diseases. By incorporating multi-source prior knowledge, including known circRNA-disease associations, disease similarities and circRNA similarities, we adopt a collective matrix completion-based graph learning model to prioritize the most promising disease-related circRNAs for guiding laborious clinical trials. The results show that iCDA-CMG outperforms other state-of-the-art models in terms of cross-validation and independent prediction. Moreover, the case studies for several representative cancers suggest the effectiveness of iCDA-CMG in screening circRNA candidates for human diseases, which will contribute to elucidating the pathogenesis mechanisms and unveiling new opportunities for disease diagnosis and targeted therapy.
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Affiliation(s)
- Qiu Xiao
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.,Hunan Xiangjiang Artificial Intelligence Academy, Changsha, 410000, China
| | - Jiancheng Zhong
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
| | - Xiwei Tang
- School of Information Science and Engineering, Hunan First Normal University, Changsha, 410205, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
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14
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Inferring Potential CircRNA–Disease Associations via Deep Autoencoder-Based Classification. Mol Diagn Ther 2020; 25:87-97. [DOI: 10.1007/s40291-020-00499-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2020] [Indexed: 01/09/2023]
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