ArticleJournal of biomedical semantics2017
DisSetSim: an online system for calculating similarity between disease sets.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- mBMC bioinformatics · 2021Article
- HN-CNN: A Heterogeneous Network Based on Convolutional Neural Network for mFrontiers in genetics · 2021Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2018Article
- BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.BMC bioinformatics · 2018Article
- Measuring phenotype-phenotype similarity through the interactome.BMC bioinformatics · 2018Article
- Effective norm emergence in cell systems under limited communication.BMC bioinformatics · 2018Article
- Higher-order partial least squares for predicting gene expression levels from chromatin states.BMC bioinformatics · 2018Article
- Automatic infection detection based on electronic medical records.BMC bioinformatics · 2018Article
- Identifying diseases-related metabolites using random walk.BMC bioinformatics · 2018Article
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8 authors.
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Abstract
backgroundFunctional 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.
resultsHere, 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.
conclusionsThe 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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