An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations
Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the association...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-05-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00466/full |
| _version_ | 1818304621561511936 |
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| author | Shuhui Su Lei Zhang Jian Liu |
| author_facet | Shuhui Su Lei Zhang Jian Liu |
| author_sort | Shuhui Su |
| collection | DOAJ |
| description | Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations.Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches. |
| first_indexed | 2024-12-13T06:13:36Z |
| format | Article |
| id | doaj.art-9c70bc85034f492fbcf16417e2db3fb4 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-13T06:13:36Z |
| publishDate | 2019-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-9c70bc85034f492fbcf16417e2db3fb42022-12-21T23:57:01ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-05-011010.3389/fgene.2019.00466454629An Effective Method to Measure Disease Similarity Using Gene and Phenotype AssociationsShuhui Su0Lei Zhang1Jian Liu2School of Computer Science and Technology, Harbin Institute of Technology, Harbin, ChinaSchool of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, ChinaMotivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations.Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches.https://www.frontiersin.org/article/10.3389/fgene.2019.00466/fulldisease similarityphenotype associationgenomic annotationdisease ontologybiomedical ontology |
| spellingShingle | Shuhui Su Lei Zhang Jian Liu An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations Frontiers in Genetics disease similarity phenotype association genomic annotation disease ontology biomedical ontology |
| title | An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations |
| title_full | An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations |
| title_fullStr | An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations |
| title_full_unstemmed | An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations |
| title_short | An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations |
| title_sort | effective method to measure disease similarity using gene and phenotype associations |
| topic | disease similarity phenotype association genomic annotation disease ontology biomedical ontology |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00466/full |
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