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| Choosing preferable labels for the Japanese translation of the Human Phenotype Ontology |
| Kota Ninomiya, Terue Takatsuki, Tatsuya Kushida, Yasunori Yamamoto, Soichi Ogishima |
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Genomics Inform. 2020;18(2):e23 Published online June 18, 2020 DOI: https://doi.org/10.5808/GI.2020.18.2.e23 |
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Choosing preferable labels for the Japanese translation of the Human Phenotype Ontology Genomics & Informatics. 2020;18(2):e23  Machine enhanced translation of the Human Phenotype Ontology project Procedia Computer Science. 2017;121:11-18 HPO2GO: prediction of human phenotype ontology term associations using cross ontology annotation co-occurrences . 2018; HPO2GO: prediction of human phenotype ontology term associations using cross ontology annotation co-occurrences . 2018; HPO2GO: prediction of human phenotype ontology term associations for proteins using cross ontology annotation co-occurrences PeerJ. 2018;6:e5298 The Human Phenotype Ontology Clinical Genetics. 2010;77(6):525-534 DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis Bioinformatics. 2019; Biological and Medical Ontologies: Human Phenotype Ontology (HPO) Encyclopedia of Bioinformatics and Computational Biology. 2019;848-857 Mapping layperson medical terminology into the Human Phenotype Ontology using neural machine translation models Expert Systems with Applications. 2022;204:117446 PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources F1000Research. 2015;4:259 |
