Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.

It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce...

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Autores principales:	Yi Zhang, Min Chen, Li Huang, Xiaolan Xie, Xin Li, Hong Jin, Xiaohua Wang, Hanyan Wei
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3bff6d57b00942d4bf8e11e5b2a941f4
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spelling	oai:doaj.org-article:3bff6d57b00942d4bf8e11e5b2a941f42021-12-02T20:16:15ZFusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.1932-620310.1371/journal.pone.0260329https://doaj.org/article/3bff6d57b00942d4bf8e11e5b2a941f42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0260329https://doaj.org/toc/1932-6203It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce the cost of biological experiments focusing on deep study furtherly. However, inaccurate construction of similarity networks and inadequate numbers of observed known lncRNA-disease associations, such inherent problems make many mature computational methods that have been developed for many years still exit some limitations. It motivates us to explore a new computational method that was fused with KATZ measure and space projection to fast probing potential lncRNA-disease associations (namely KATZSP). KATZSP is comprised of following key steps: combining all the global information with which to change Boolean network of known lncRNA-disease associations into the weighted networks; changing the similarities calculation into counting the number of walks that connect lncRNA nodes and disease nodes in bipartite graphs; obtaining the space projection scores to refine the primary prediction scores. The process to fuse KATZ measure and space projection was simplified and uncomplicated with needing only one attenuation factor. The leave-one-out cross validation (LOOCV) experimental results showed that, compared with other state-of-the-art methods (NCPLDA, LDAI-ISPS and IIRWR), KATZSP had a higher predictive accuracy shown with area-under-the-curve (AUC) value on the three datasets built, while KATZSP well worked on inferring potential associations related to new lncRNAs (or isolated diseases). The results from real cases study (such as pancreas cancer, lung cancer and colorectal cancer) further confirmed that KATZSP is capable of superior predictive ability to be applied as a guide for traditional biological experiments.Yi ZhangMin ChenLi HuangXiaolan XieXin LiHong JinXiaohua WangHanyan WeiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0260329 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yi Zhang Min Chen Li Huang Xiaolan Xie Xin Li Hong Jin Xiaohua Wang Hanyan Wei Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.
description	It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce the cost of biological experiments focusing on deep study furtherly. However, inaccurate construction of similarity networks and inadequate numbers of observed known lncRNA-disease associations, such inherent problems make many mature computational methods that have been developed for many years still exit some limitations. It motivates us to explore a new computational method that was fused with KATZ measure and space projection to fast probing potential lncRNA-disease associations (namely KATZSP). KATZSP is comprised of following key steps: combining all the global information with which to change Boolean network of known lncRNA-disease associations into the weighted networks; changing the similarities calculation into counting the number of walks that connect lncRNA nodes and disease nodes in bipartite graphs; obtaining the space projection scores to refine the primary prediction scores. The process to fuse KATZ measure and space projection was simplified and uncomplicated with needing only one attenuation factor. The leave-one-out cross validation (LOOCV) experimental results showed that, compared with other state-of-the-art methods (NCPLDA, LDAI-ISPS and IIRWR), KATZSP had a higher predictive accuracy shown with area-under-the-curve (AUC) value on the three datasets built, while KATZSP well worked on inferring potential associations related to new lncRNAs (or isolated diseases). The results from real cases study (such as pancreas cancer, lung cancer and colorectal cancer) further confirmed that KATZSP is capable of superior predictive ability to be applied as a guide for traditional biological experiments.
format	article
author	Yi Zhang Min Chen Li Huang Xiaolan Xie Xin Li Hong Jin Xiaohua Wang Hanyan Wei
author_facet	Yi Zhang Min Chen Li Huang Xiaolan Xie Xin Li Hong Jin Xiaohua Wang Hanyan Wei
author_sort	Yi Zhang
title	Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.
title_short	Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.
title_full	Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.
title_fullStr	Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.
title_full_unstemmed	Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.
title_sort	fusion of katz measure and space projection to fast probe potential lncrna-disease associations in bipartite graphs.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3bff6d57b00942d4bf8e11e5b2a941f4
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Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.

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