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DisSetSim: an online system for calculating similarity between disease sets

BACKGROUND: Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS)...

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Detalles Bibliográficos
Autores principales: Hu, Yang, Zhao, Lingling, Liu, Zhiyan, Ju, Hong, Shi, Hongbo, Xu, Peigang, Wang, Yadong, Cheng, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763469/
https://www.ncbi.nlm.nih.gov/pubmed/29297411
http://dx.doi.org/10.1186/s13326-017-0140-2
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author Hu, Yang
Zhao, Lingling
Liu, Zhiyan
Ju, Hong
Shi, Hongbo
Xu, Peigang
Wang, Yadong
Cheng, Liang
author_facet Hu, Yang
Zhao, Lingling
Liu, Zhiyan
Ju, Hong
Shi, Hongbo
Xu, Peigang
Wang, Yadong
Cheng, Liang
author_sort Hu, Yang
collection PubMed
description BACKGROUND: Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS) limits this type of application. RESULTS: Here, we introduce DisSetSim, an online system to solve this problem in this article. Five state-of-the-art methods involving Resnik’s, Lin’s, Wang’s, PSB, and SemFunSim methods were implemented to measure the similarity score of pair-wise diseases (SSD) first. And then “pair-wise-best pairs-average” (PWBPA) method was implemented to calculated the SSDS by the SSD. The system was applied for calculating the functional similarity of miRNAs based on their induced disease sets. The results were further used to predict potential disease-miRNA relationships. CONCLUSIONS: The high area under the receiver operating characteristic curve AUC (0.9296) based on leave-one-out cross validation shows that the PWBPA method achieves a high true positive rate and a low false positive rate. The system can be accessed from http://www.bio-annotation.cn:8080/DisSetSim/.
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spelling pubmed-57634692018-01-17 DisSetSim: an online system for calculating similarity between disease sets Hu, Yang Zhao, Lingling Liu, Zhiyan Ju, Hong Shi, Hongbo Xu, Peigang Wang, Yadong Cheng, Liang J Biomed Semantics Research BACKGROUND: Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS) limits this type of application. RESULTS: Here, we introduce DisSetSim, an online system to solve this problem in this article. Five state-of-the-art methods involving Resnik’s, Lin’s, Wang’s, PSB, and SemFunSim methods were implemented to measure the similarity score of pair-wise diseases (SSD) first. And then “pair-wise-best pairs-average” (PWBPA) method was implemented to calculated the SSDS by the SSD. The system was applied for calculating the functional similarity of miRNAs based on their induced disease sets. The results were further used to predict potential disease-miRNA relationships. CONCLUSIONS: The high area under the receiver operating characteristic curve AUC (0.9296) based on leave-one-out cross validation shows that the PWBPA method achieves a high true positive rate and a low false positive rate. The system can be accessed from http://www.bio-annotation.cn:8080/DisSetSim/. BioMed Central 2017-09-20 /pmc/articles/PMC5763469/ /pubmed/29297411 http://dx.doi.org/10.1186/s13326-017-0140-2 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Hu, Yang
Zhao, Lingling
Liu, Zhiyan
Ju, Hong
Shi, Hongbo
Xu, Peigang
Wang, Yadong
Cheng, Liang
DisSetSim: an online system for calculating similarity between disease sets
title DisSetSim: an online system for calculating similarity between disease sets
title_full DisSetSim: an online system for calculating similarity between disease sets
title_fullStr DisSetSim: an online system for calculating similarity between disease sets
title_full_unstemmed DisSetSim: an online system for calculating similarity between disease sets
title_short DisSetSim: an online system for calculating similarity between disease sets
title_sort dissetsim: an online system for calculating similarity between disease sets
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763469/
https://www.ncbi.nlm.nih.gov/pubmed/29297411
http://dx.doi.org/10.1186/s13326-017-0140-2
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