Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.

<h4>Background</h4>The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of the...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ping Xuan, Ke Han, Maozu Guo, Yahong Guo, Jinbao Li, Jian Ding, Yong Liu, Qiguo Dai, Jin Li, Zhixia Teng, Yufei Huang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/065252b329b546a5b742ca3fe8994d0c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:065252b329b546a5b742ca3fe8994d0c
record_format	dspace
spelling	oai:doaj.org-article:065252b329b546a5b742ca3fe8994d0c2021-11-18T09:00:38ZPrediction of microRNAs associated with human diseases based on weighted k most similar neighbors.1932-620310.1371/journal.pone.0070204https://doaj.org/article/065252b329b546a5b742ca3fe8994d0c2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23950912/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies.<h4>Methodology/principal findings</h4>It is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted k most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates.<h4>Conclusions</h4>The superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted k most similar neighbors. The online prediction and analysis tool is freely available at http://nclab.hit.edu.cn/hdmpred.Ping XuanKe HanMaozu GuoYahong GuoJinbao LiJian DingYong LiuQiguo DaiJin LiZhixia TengYufei HuangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e70204 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ping Xuan Ke Han Maozu Guo Yahong Guo Jinbao Li Jian Ding Yong Liu Qiguo Dai Jin Li Zhixia Teng Yufei Huang Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
description	<h4>Background</h4>The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies.<h4>Methodology/principal findings</h4>It is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted k most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates.<h4>Conclusions</h4>The superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted k most similar neighbors. The online prediction and analysis tool is freely available at http://nclab.hit.edu.cn/hdmpred.
format	article
author	Ping Xuan Ke Han Maozu Guo Yahong Guo Jinbao Li Jian Ding Yong Liu Qiguo Dai Jin Li Zhixia Teng Yufei Huang
author_facet	Ping Xuan Ke Han Maozu Guo Yahong Guo Jinbao Li Jian Ding Yong Liu Qiguo Dai Jin Li Zhixia Teng Yufei Huang
author_sort	Ping Xuan
title	Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
title_short	Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
title_full	Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
title_fullStr	Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
title_full_unstemmed	Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
title_sort	prediction of micrornas associated with human diseases based on weighted k most similar neighbors.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/065252b329b546a5b742ca3fe8994d0c
work_keys_str_mv	AT pingxuan predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT kehan predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT maozuguo predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT yahongguo predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT jinbaoli predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT jianding predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT yongliu predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT qiguodai predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT jinli predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT zhixiateng predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors AT yufeihuang predictionofmicrornasassociatedwithhumandiseasesbasedonweightedkmostsimilarneighbors
_version_	1718421004583174144

Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.

Ejemplares similares