A disease similarity matrix based on the uniqueness of shared genes
Abstract Background Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a re...
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doaj-2ba3d652f4eb41f8b5cf407977f884e22021-04-02T11:19:30ZengBMCBMC Medical Genomics1755-87942017-05-0110S1273210.1186/s12920-017-0265-2A disease similarity matrix based on the uniqueness of shared genesMatthew B. Carson0Cong Liu1Yao Lu2Caiyan Jia3Hui Lu4Department of Preventive Medicine, Feinberg School of Medicine, Northwestern UniversityDepartment of Bioengineering, University of Illinois at ChicagoCenter for Biomedical Informatics, Shanghai Children’s HospitalDepartment of Computer Science, Beijing Jiaotong UniversityDepartment of Bioengineering, University of Illinois at ChicagoAbstract Background Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing. Methods Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation. Results Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study. Conclusions We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases.http://link.springer.com/article/10.1186/s12920-017-0265-2Disease-disease similarityDisease-related genesClustering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Matthew B. Carson Cong Liu Yao Lu Caiyan Jia Hui Lu |
spellingShingle |
Matthew B. Carson Cong Liu Yao Lu Caiyan Jia Hui Lu A disease similarity matrix based on the uniqueness of shared genes BMC Medical Genomics Disease-disease similarity Disease-related genes Clustering |
author_facet |
Matthew B. Carson Cong Liu Yao Lu Caiyan Jia Hui Lu |
author_sort |
Matthew B. Carson |
title |
A disease similarity matrix based on the uniqueness of shared genes |
title_short |
A disease similarity matrix based on the uniqueness of shared genes |
title_full |
A disease similarity matrix based on the uniqueness of shared genes |
title_fullStr |
A disease similarity matrix based on the uniqueness of shared genes |
title_full_unstemmed |
A disease similarity matrix based on the uniqueness of shared genes |
title_sort |
disease similarity matrix based on the uniqueness of shared genes |
publisher |
BMC |
series |
BMC Medical Genomics |
issn |
1755-8794 |
publishDate |
2017-05-01 |
description |
Abstract Background Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing. Methods Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation. Results Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study. Conclusions We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases. |
topic |
Disease-disease similarity Disease-related genes Clustering |
url |
http://link.springer.com/article/10.1186/s12920-017-0265-2 |
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