Robinson, Peter N.

The human phenotype ontology: a tool for annotating and analyzing human hereditary disease

Robinson, P. N., Köhler, S., Bauer, S., Seelow, D., Horn, D., & Mundlos, S. (2008) · The American Journal of Human Genetics

Biomedical Text Mining and OntologiesGenomics and Rare Diseases

Cited by 1,034

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

Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies

Köhler, S., Schulz, M. H., Krawitz, P., Bauer, S., Dölken, S., Ott, C. E., Mundlos, C., Horn, D., Mundlos, S., & Robinson, P. N. (2009) · The American Journal of Human Genetics

Biomedical Text Mining and OntologiesGenomics and Rare Diseases

Cited by 590

How many rare diseases are there?

Haendel, M., Vasilevsky, N., Unni, D., Bologa, C., Harris, N., Rehm, H., Hamosh, A., Baynam, G., Groza, T., McMurry, J., et al. (2020) · Nature Reviews Drug Discovery

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 554

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment

Haendel, M. A., Chute, C. G., Bennett, T. D., Eichmann, D. A., Guinney, J., Kibbe, W. A., Payne, P. R. O., Pfaff, E. R., Robinson, P. N., Saltz, J. H., et al. (2021) · Journal of the American Medical Informatics Association

Research Data Management PracticesElectronic Health Records Systems

Cited by 553

International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases

Boycott, K. M., Rath, A., Chong, J. X., Hartley, T., Alkuraya, F. S., Baynam, G., Brookes, A. J., Brudno, M., Carracedo, A., den Dunnen, J. T., et al. (2017) · The American Journal of Human Genetics

Genomics and Rare DiseasesConnective tissue disorders research

Cited by 447

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

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

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

Improved exome prioritization of disease genes through cross-species phenotype comparison

Robinson, P. N., Köhler, S., Oellrich, A., Sanger Mouse Genetics Project, Wang, K., Mungall, C. J., Lewis, S. E., Washington, N., Bauer, S., Seelow, D., et al. (2014) · Genome Research

Genomics and Rare DiseasesBioinformatics and Genomic Networks

Cited by 375

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

Characterizing long COVID: deep phenotype of a complex condition

Deer, R. R., Rock, M. A., Vasilevsky, N., Carmody, L., Rando, H., Anzalone, A. J., Basson, M. D., Bennett, T. D., Bergquist, T., Boudreau, E. A., et al. (2021) · EBioMedicine

Long-Term Effects of COVID-19Biomedical Text Mining and Ontologies

Cited by 200

Deletions of chromosomal regulatory boundaries are associated with congenital disease

Ibn-Salem, J., Köhler, S., Love, M. I., Chung, H.-R., Huang, N., Hurles, M. E., Haendel, M., Washington, N. L., Smedley, D., Mungall, C. J., et al. (2014) · Genome Biology

Genomic variations and chromosomal abnormalitiesGenomics and Chromatin Dynamics

Cited by 143

Mondo: Unifying diseases for the world, by the world

Vasilevsky, N. A., Matentzoglu, N. A., Toro, S., Flack, J. E., Hegde, H., Unni, D. R., Alyea, G. F., Amberger, J. S., Babb, L., Balhoff, J. P., et al. (2022) · medRxiv

Biomedical Text Mining and OntologiesSemantic Web and Ontologies

Cited by 107

Islet Autoantibody Standardization Program 2018 workshop: interlaboratory comparison of glutamic acid decarboxylase autoantibody assay performance

Lampasona, V., Pittman, D. L., Williams, A. J., Achenbach, P., Schlosser, M., Akolkar, B., Winter, W. E., Laboratories, P., Watson, K., Weets, I., et al. (2019) · Clinical Chemistry

Diabetes and associated disordersClinical Laboratory Practices and Quality Control

Cited by 103

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants

Schubach, M., Re, M., Robinson, P. N., & Valentini, G. (2017) · Scientific Reports

Genetic Associations and EpidemiologyGenomics and Rare Diseases

Cited by 97

A Census of Disease Ontologies

Haendel, M. A., McMurry, J. A., Relevo, R., Mungall, C. J., Robinson, P. N., & Chute, C. G. (2018) · Annual Review of Biomedical Data Science

Biomedical Text Mining and OntologiesSemantic Web and Ontologies

Cited by 49

The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process

Heinrich, V., Stange, J., Dickhaus, T., Imkeller, P., Krüger, U., Bauer, S., Mundlos, S., Robinson, P. N., Hecht, J., & Krawitz, P. M. (2012) · Nucleic Acids Research

Genomics and Phylogenetic StudiesGenomics and Rare Diseases

Cited by 43

An ontology-based knowledge graph for representing interactions involving RNA molecules

Cavalleri, E., Cabri, A., Soto-Gomez, M., Bonfitto, S., Perlasca, P., Gliozzo, J., Callahan, T. J., Reese, J., Robinson, P. N., Casiraghi, E., et al. (2024) · Scientific Data

Biomedical Text Mining and OntologiesBioinformatics and Genomic Networks

Cited by 33

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