Jing Chen1, Huan Xu2, Anil Jegga3, Kejian Zhang4,5, Pete S White3,4, Ge Zhang6,7. 1. Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. jing.chen2@cchmc.org. 2. Division of Biostatistics and Bioinformatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 3. Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 4. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 5. Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 6. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. ge.zhang@cchmc.org. 7. Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. ge.zhang@cchmc.org.
Abstract
PURPOSE: To improve the accuracy of matching rare genetic diseases based on patient's phenotypes. METHODS: We introduce new methods to prioritize diagnosis of genetic diseases based on integrated semantic similarity (method 1) and ontological overlap (method 2) between the phenotypes expressed by a patient and phenotypes annotated to known diseases. RESULTS: We evaluated the performance of our methods by two sets of simulated data and one set of patient's data derived from electronic health records. We demonstrated that the two methods achieved significantly improved performance compared with previous methods in correctly prioritizing candidate diseases in all of the three sets. Our methods are freely available as a web application ( https://gddp. RESEARCH: cchmc.org/ ) to aid diagnosis of genetic diseases. CONCLUSION: Our methods can capture the diagnostic information embedded in the phenotype ontology, consider all phenotypes exhibited by a patient, and are more robust than the existing methods when phenotypes are incorrectly or imprecisely specified. These methods can assist the diagnosis of rare genetic diseases and help the interpretation of the results of DNA tests.
PURPOSE: To improve the accuracy of matching rare genetic diseases based on patient's phenotypes. METHODS: We introduce new methods to prioritize diagnosis of genetic diseases based on integrated semantic similarity (method 1) and ontological overlap (method 2) between the phenotypes expressed by a patient and phenotypes annotated to known diseases. RESULTS: We evaluated the performance of our methods by two sets of simulated data and one set of patient's data derived from electronic health records. We demonstrated that the two methods achieved significantly improved performance compared with previous methods in correctly prioritizing candidate diseases in all of the three sets. Our methods are freely available as a web application ( https://gddp. RESEARCH: cchmc.org/ ) to aid diagnosis of genetic diseases. CONCLUSION: Our methods can capture the diagnostic information embedded in the phenotype ontology, consider all phenotypes exhibited by a patient, and are more robust than the existing methods when phenotypes are incorrectly or imprecisely specified. These methods can assist the diagnosis of rare genetic diseases and help the interpretation of the results of DNA tests.
Entities:
Keywords:
Diagnosis; Human Phenotype Ontology; Mendelian disease
Authors: Sofia Barbosa-Gouveia; María E Vázquez-Mosquera; Emiliano González-Vioque; José V Álvarez; Roi Chans; Francisco Laranjeira; Esmeralda Martins; Ana Cristina Ferreira; Alejandro Avila-Alvarez; María L Couce Journal: Genes (Basel) Date: 2021-08-19 Impact factor: 4.096
Authors: Sofia Barbosa-Gouveia; Maria Eugenia Vázquez-Mosquera; Emiliano González-Vioque; Álvaro Hermida-Ameijeiras; Paula Sánchez-Pintos; Maria José de Castro; Soraya Ramiro León; Belén Gil-Fournier; Cristina Domínguez-González; Ana Camacho Salas; Luis Negrão; Isabel Fineza; Francisco Laranjeira; Maria Luz Couce Journal: J Clin Med Date: 2022-05-12 Impact factor: 4.964