Jacobsen, Julius O B
Queen Mary University of London · William Harvey Research Institute
6 papers on CoChatORCID 0000-0002-3265-1591
Most cited papers
- Verified
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources
Köhler, S., Carmody, L., Vasilevsky, N., Jacobsen, J. O. B., Danis, D., Gourdine, J.-P., Gargano, M., Harris, N. L., Matentzoglu, N., McMurry, J. A., et al. (2019) · Nucleic Acids Research
Biomedical Text Mining and OntologiesGenomics and Rare Diseases
Cited by 677
- Verified
Next-generation diagnostics and disease-gene discovery with the Exomiser
Smedley, D., Jacobsen, J. O. B., Jäger, M., Köhler, S., Holtgrewe, M., Schubach, M., Siragusa, E., Zemojtel, T., Buske, O. J., Washington, N. L., et al. (2015) · Nature Protocols
Genomics and Rare DiseasesBioinformatics and Genomic Networks
Cited by 445
- Verified
The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species
Mungall, C. J., McMurry, J. A., Köhler, S., Balhoff, J. P., Borromeo, C., Brush, M., Carbon, S., Conlin, T., Dunn, N., Engelstad, M., et al. (2017) · Nucleic Acids Research
Biomedical Text Mining and OntologiesBioinformatics and Genomic Networks
Cited by 441
- Verified
The Human Phenotype Ontology in 2024: phenotypes around the world
Gargano, M. A., Matentzoglu, N., Coleman, B., Addo-Lartey, E. B., Anagnostopoulos, A. V., Anderton, J., Avillach, P., Bagley, A. M., Bakštein, E., Balhoff, J. P., et al. (2024) · Nucleic Acids Research
Biomedical Text Mining and OntologiesGenomics and Rare Diseases
Cited by 410
- Verified
A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease
Smedley, D., Schubach, M., Jacobsen, J. O. B., Köhler, S., Zemojtel, T., Spielmann, M., Jäger, M., Hochheiser, H., Washington, N. L., McMurry, J. A., et al. (2016) · The American Journal of Human Genetics
Genomics and Rare DiseasesGenetic Associations and Epidemiology
Cited by 283
- Verified
Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools
Reese, J. T., Chimirri, L., Bridges, Y., Danis, D., Caufield, J. H., Gargano, M. A., Kroll, C., Schmeder, A., Liu, F., Wissink, K., et al. (2024) · medRxiv
Artificial Intelligence in Healthcare and EducationGenomics and Rare Diseases
Cited by 8
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