Predicting human disease-associated circRNAs based on locality-constrained linear coding

Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to unders...

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Published in:	Genomics (San Diego, Calif.) Vol. 112; no. 2; pp. 1335 - 1342
Main Authors:	Ge, Erxia, Yang, Yingjuan, Gang, Mingjun, Fan, Chunlong, Zhao, Qi
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.03.2020
Subjects:	Algorithms Association prediction breast neoplasms carcinoma case studies circRNA circular RNA computational methodology Cosine similarity Disease Genetic Predisposition to Disease Genome-Wide Association Study - methods human diseases Humans Label propagation Locality-constrained linear coding Neoplasms - genetics non-coding RNA pathogenesis prediction RNA, Circular - genetics stomach neoplasms Cosine similarity LLC circRNA Disease ROC FPR DAG Label propagation AUC SVT Locality-constrained linear coding TPR HPRD Association prediction 5-fold CV
ISSN:	0888-7543, 1089-8646, 1089-8646
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Abstract	Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs. •The computational model made use of locality-constrained linear coding and label propagation.•The AUC of 5-fold cross validation (5-fold CV) were significantly better than other previous computational methods.•Three case studies for important human diseases were implemented.•LLCDC could be a reliable method for human disease associated circRNAs prediction.
AbstractList	Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs. Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs. •The computational model made use of locality-constrained linear coding and label propagation.•The AUC of 5-fold cross validation (5-fold CV) were significantly better than other previous computational methods.•Three case studies for important human diseases were implemented.•LLCDC could be a reliable method for human disease associated circRNAs prediction. Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs.Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs.
Author	Zhao, Qi Ge, Erxia Gang, Mingjun Fan, Chunlong Yang, Yingjuan
Author_xml	– sequence: 1 givenname: Erxia surname: Ge fullname: Ge, Erxia organization: School of Mathematics, Liaoning University, Shenyang 110036, China – sequence: 2 givenname: Yingjuan surname: Yang fullname: Yang, Yingjuan organization: School of Mathematics, Liaoning University, Shenyang 110036, China – sequence: 3 givenname: Mingjun surname: Gang fullname: Gang, Mingjun organization: School of Mathematics, Jilin University, Changchun 130000, China – sequence: 4 givenname: Chunlong surname: Fan fullname: Fan, Chunlong organization: College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China – sequence: 5 givenname: Qi surname: Zhao fullname: Zhao, Qi email: zhaoqi@lnu.edu.cn organization: College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
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Keywords	Cosine similarity LLC circRNA Disease ROC FPR DAG Label propagation AUC SVT Locality-constrained linear coding TPR HPRD Association prediction 5-fold CV
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Snippet	Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs...
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SubjectTerms	Algorithms Association prediction breast neoplasms carcinoma case studies circRNA circular RNA computational methodology Cosine similarity Disease Genetic Predisposition to Disease Genome-Wide Association Study - methods human diseases Humans Label propagation Locality-constrained linear coding Neoplasms - genetics non-coding RNA pathogenesis prediction RNA, Circular - genetics stomach neoplasms
Title	Predicting human disease-associated circRNAs based on locality-constrained linear coding
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