Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph

Abstract Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendou...

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Autores principales:	Van Tinh Nguyen, Thi Tu Kien Le, Tran Quoc Vinh Nguyen, Dang Hung Tran
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Infer miRNA-disease associations miRNA-disease-lncRNA tripartite graph Collaborative filtering algorithm Resource allocation algorithm Recommender systems Internal medicine RC31-1245 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/49e8cd29aeea49c6bdd273310a43f544
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spelling	oai:doaj.org-article:49e8cd29aeea49c6bdd273310a43f5442021-11-21T12:04:19ZInferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph10.1186/s12920-021-01078-81755-8794https://doaj.org/article/49e8cd29aeea49c6bdd273310a43f5442021-11-01T00:00:00Zhttps://doi.org/10.1186/s12920-021-01078-8https://doaj.org/toc/1755-8794Abstract Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.Van Tinh NguyenThi Tu Kien LeTran Quoc Vinh NguyenDang Hung TranBMCarticleInfer miRNA-disease associationsmiRNA-disease-lncRNA tripartite graphCollaborative filtering algorithmResource allocation algorithmRecommender systemsInternal medicineRC31-1245GeneticsQH426-470ENBMC Medical Genomics, Vol 14, Iss S3, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Infer miRNA-disease associations miRNA-disease-lncRNA tripartite graph Collaborative filtering algorithm Resource allocation algorithm Recommender systems Internal medicine RC31-1245 Genetics QH426-470
spellingShingle	Infer miRNA-disease associations miRNA-disease-lncRNA tripartite graph Collaborative filtering algorithm Resource allocation algorithm Recommender systems Internal medicine RC31-1245 Genetics QH426-470 Van Tinh Nguyen Thi Tu Kien Le Tran Quoc Vinh Nguyen Dang Hung Tran Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph
description	Abstract Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.
format	article
author	Van Tinh Nguyen Thi Tu Kien Le Tran Quoc Vinh Nguyen Dang Hung Tran
author_facet	Van Tinh Nguyen Thi Tu Kien Le Tran Quoc Vinh Nguyen Dang Hung Tran
author_sort	Van Tinh Nguyen
title	Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph
title_short	Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph
title_full	Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph
title_fullStr	Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph
title_full_unstemmed	Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph
title_sort	inferring mirna-disease associations using collaborative filtering and resource allocation on a tripartite graph
publisher	BMC
publishDate	2021
url	https://doaj.org/article/49e8cd29aeea49c6bdd273310a43f544
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Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph

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