A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology
Abstract Background Although rapid developed sequencing technologies make it possible for genotype data to be used in clinical diagnosis, it is still challenging for clinicians to understand the results of sequencing and make correct judgement based on them. Before this, diagnosis based on clinical...
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| Format: | Article |
| Language: | English |
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BMC
2018-05-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2064-y |
| _version_ | 1819029146711359488 |
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| author | Xiaofeng Gong Jianping Jiang Zhongqu Duan Hui Lu |
| author_facet | Xiaofeng Gong Jianping Jiang Zhongqu Duan Hui Lu |
| author_sort | Xiaofeng Gong |
| collection | DOAJ |
| description | Abstract Background Although rapid developed sequencing technologies make it possible for genotype data to be used in clinical diagnosis, it is still challenging for clinicians to understand the results of sequencing and make correct judgement based on them. Before this, diagnosis based on clinical features held a leading position. With the establishment of the Human Phenotype Ontology (HPO) and the enrichment of phenotype-disease annotations, there throws much more attention to the improvement of phenotype-based diagnosis. Results In this study, we presented a novel method called RelativeBestPair to measure similarity from the query terms to hereditary diseases based on HPO and then rank the candidate diseases. To evaluate the performance, we simulated a set of patients based on 44 complex diseases. Besides, by adding noise or imprecision or both, cases closer to real clinical conditions were generated. Thus, four simulated datasets were used to make comparison among RelativeBestPair and seven existing semantic similarity measures. RelativeBestPair ranked the underlying disease as top 1 on 93.73% of the simulated dataset without noise and imprecision, 93.64% of the simulated dataset with noise and without imprecision, 39.82% of the simulated dataset without noise and with imprecision, and 33.64% of the simulated dataset with both noise and imprecision. Conclusion Compared with the seven existing semantic similarity measures, RelativeBestPair showed similar performance in two datasets without imprecision. While RelativeBestPair appeared to be equal to Resnik and better than other six methods in the simulated dataset without noise and with imprecision, it significantly outperformed all other seven methods in the simulated dataset with both noise and imprecision. It can be indicated that RelativeBestPair might be of great help in clinical setting. |
| first_indexed | 2024-12-21T06:09:37Z |
| format | Article |
| id | doaj.art-cd83fb6f4e984ef882c88e345c5280ce |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T06:09:37Z |
| publishDate | 2018-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-cd83fb6f4e984ef882c88e345c5280ce2022-12-21T19:13:34ZengBMCBMC Bioinformatics1471-21052018-05-0119S411111910.1186/s12859-018-2064-yA new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontologyXiaofeng Gong0Jianping Jiang1Zhongqu Duan2Hui Lu3Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong UniversityDepartment of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong UniversityDepartment of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong UniversityDepartment of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong UniversityAbstract Background Although rapid developed sequencing technologies make it possible for genotype data to be used in clinical diagnosis, it is still challenging for clinicians to understand the results of sequencing and make correct judgement based on them. Before this, diagnosis based on clinical features held a leading position. With the establishment of the Human Phenotype Ontology (HPO) and the enrichment of phenotype-disease annotations, there throws much more attention to the improvement of phenotype-based diagnosis. Results In this study, we presented a novel method called RelativeBestPair to measure similarity from the query terms to hereditary diseases based on HPO and then rank the candidate diseases. To evaluate the performance, we simulated a set of patients based on 44 complex diseases. Besides, by adding noise or imprecision or both, cases closer to real clinical conditions were generated. Thus, four simulated datasets were used to make comparison among RelativeBestPair and seven existing semantic similarity measures. RelativeBestPair ranked the underlying disease as top 1 on 93.73% of the simulated dataset without noise and imprecision, 93.64% of the simulated dataset with noise and without imprecision, 39.82% of the simulated dataset without noise and with imprecision, and 33.64% of the simulated dataset with both noise and imprecision. Conclusion Compared with the seven existing semantic similarity measures, RelativeBestPair showed similar performance in two datasets without imprecision. While RelativeBestPair appeared to be equal to Resnik and better than other six methods in the simulated dataset without noise and with imprecision, it significantly outperformed all other seven methods in the simulated dataset with both noise and imprecision. It can be indicated that RelativeBestPair might be of great help in clinical setting.http://link.springer.com/article/10.1186/s12859-018-2064-yHuman phenotype ontology (HPO)Semantic similarityDiseaseDiagnosis |
| spellingShingle | Xiaofeng Gong Jianping Jiang Zhongqu Duan Hui Lu A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology BMC Bioinformatics Human phenotype ontology (HPO) Semantic similarity Disease Diagnosis |
| title | A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology |
| title_full | A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology |
| title_fullStr | A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology |
| title_full_unstemmed | A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology |
| title_short | A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology |
| title_sort | new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology |
| topic | Human phenotype ontology (HPO) Semantic similarity Disease Diagnosis |
| url | http://link.springer.com/article/10.1186/s12859-018-2064-y |
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