SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec

Abstract Background Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated,...

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Autores principales:	Jianwei Li, Jianing Li, Mengfan Kong, Duanyang Wang, Kun Fu, Jiangcheng Shi
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	LncRNA-disease association prediction Singular Value Decomposition node2vec Network representation learning XGBoost classifier Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ffe9b042f8344a1f8207d7bcb6f0d261
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spelling	oai:doaj.org-article:ffe9b042f8344a1f8207d7bcb6f0d2612021-11-07T12:22:19ZSVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec10.1186/s12859-021-04457-11471-2105https://doaj.org/article/ffe9b042f8344a1f8207d7bcb6f0d2612021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04457-1https://doaj.org/toc/1471-2105Abstract Background Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy. Results In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually. Conclusions We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.Jianwei LiJianing LiMengfan KongDuanyang WangKun FuJiangcheng ShiBMCarticleLncRNA-disease association predictionSingular Value Decompositionnode2vecNetwork representation learningXGBoost classifierComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	LncRNA-disease association prediction Singular Value Decomposition node2vec Network representation learning XGBoost classifier Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	LncRNA-disease association prediction Singular Value Decomposition node2vec Network representation learning XGBoost classifier Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Jianwei Li Jianing Li Mengfan Kong Duanyang Wang Kun Fu Jiangcheng Shi SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec
description	Abstract Background Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy. Results In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually. Conclusions We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.
format	article
author	Jianwei Li Jianing Li Mengfan Kong Duanyang Wang Kun Fu Jiangcheng Shi
author_facet	Jianwei Li Jianing Li Mengfan Kong Duanyang Wang Kun Fu Jiangcheng Shi
author_sort	Jianwei Li
title	SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec
title_short	SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec
title_full	SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec
title_fullStr	SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec
title_full_unstemmed	SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec
title_sort	svdnvlda: predicting lncrna-disease associations by singular value decomposition and node2vec
publisher	BMC
publishDate	2021
url	https://doaj.org/article/ffe9b042f8344a1f8207d7bcb6f0d261
work_keys_str_mv	AT jianweili svdnvldapredictinglncrnadiseaseassociationsbysingularvaluedecompositionandnode2vec AT jianingli svdnvldapredictinglncrnadiseaseassociationsbysingularvaluedecompositionandnode2vec AT mengfankong svdnvldapredictinglncrnadiseaseassociationsbysingularvaluedecompositionandnode2vec AT duanyangwang svdnvldapredictinglncrnadiseaseassociationsbysingularvaluedecompositionandnode2vec AT kunfu svdnvldapredictinglncrnadiseaseassociationsbysingularvaluedecompositionandnode2vec AT jiangchengshi svdnvldapredictinglncrnadiseaseassociationsbysingularvaluedecompositionandnode2vec
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SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec

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