SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association

Background Measuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectiv...

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Main Authors: Cheng, Liang, Li, Jie, Ju, Peng, Peng, Jiajie, Wang, Yadong
Other Authors: Di Cunto, Ferdinando
Format: Article
Language:English
Published: 2014
Online Access:https://hdl.handle.net/10356/104881
http://hdl.handle.net/10220/20272
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1048812022-02-16T16:29:52Z SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association Cheng, Liang Li, Jie Ju, Peng Peng, Jiajie Wang, Yadong Di Cunto, Ferdinando School of Electrical and Electronic Engineering Background Measuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectives. Currently, it is still a challenge to exploit both of them to calculate disease similarity. Therefore, a new method (SemFunSim) that integrates semantic and functional association is proposed to address the issue. Methods SemFunSim is designed as follows. First of all, FunSim (Functional similarity) is proposed to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. Next, SemSim (Semantic Similarity) is devised to calculate disease similarity using the relationship between two diseases from Disease Ontology. Finally, FunSim and SemSim are integrated to measure disease similarity. Results The high average AUC (area under the receiver operating characteristic curve) (96.37%) shows that SemFunSim achieves a high true positive rate and a low false positive rate. 79 of the top 100 pairs of similar diseases identified by SemFunSim are annotated in the Comparative Toxicogenomics Database (CTD) as being targeted by the same therapeutic compounds, while other methods we compared could identify 35 or less such pairs among the top 100. Moreover, when using our method on diseases without annotated compounds in CTD, we could confirm many of our predicted candidate compounds from literature. This indicates that SemFunSim is an effective method for drug repositioning. Published version 2014-08-14T04:42:39Z 2019-12-06T21:41:52Z 2014-08-14T04:42:39Z 2019-12-06T21:41:52Z 2014 2014 Journal Article Cheng, L., Li, J., Ju, P., Peng, J., & Wang, Y. (2014). SemFunSim: A New Method for Measuring Disease Similarity by Integrating Semantic and Gene Functional Association. PLoS ONE, 9(6), e99415-. 1932-6203 https://hdl.handle.net/10356/104881 http://hdl.handle.net/10220/20272 10.1371/journal.pone.0099415 24932637 en PLoS ONE © 2014 Cheng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
description Background Measuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectives. Currently, it is still a challenge to exploit both of them to calculate disease similarity. Therefore, a new method (SemFunSim) that integrates semantic and functional association is proposed to address the issue. Methods SemFunSim is designed as follows. First of all, FunSim (Functional similarity) is proposed to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. Next, SemSim (Semantic Similarity) is devised to calculate disease similarity using the relationship between two diseases from Disease Ontology. Finally, FunSim and SemSim are integrated to measure disease similarity. Results The high average AUC (area under the receiver operating characteristic curve) (96.37%) shows that SemFunSim achieves a high true positive rate and a low false positive rate. 79 of the top 100 pairs of similar diseases identified by SemFunSim are annotated in the Comparative Toxicogenomics Database (CTD) as being targeted by the same therapeutic compounds, while other methods we compared could identify 35 or less such pairs among the top 100. Moreover, when using our method on diseases without annotated compounds in CTD, we could confirm many of our predicted candidate compounds from literature. This indicates that SemFunSim is an effective method for drug repositioning.
author2 Di Cunto, Ferdinando
author_facet Di Cunto, Ferdinando
Cheng, Liang
Li, Jie
Ju, Peng
Peng, Jiajie
Wang, Yadong
format Article
author Cheng, Liang
Li, Jie
Ju, Peng
Peng, Jiajie
Wang, Yadong
spellingShingle Cheng, Liang
Li, Jie
Ju, Peng
Peng, Jiajie
Wang, Yadong
SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association
author_sort Cheng, Liang
title SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association
title_short SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association
title_full SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association
title_fullStr SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association
title_full_unstemmed SemFunSim : a new method for measuring disease similarity by integrating semantic and gene functional association
title_sort semfunsim : a new method for measuring disease similarity by integrating semantic and gene functional association
publishDate 2014
url https://hdl.handle.net/10356/104881
http://hdl.handle.net/10220/20272
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