The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index.
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources
Open accessAbstract
The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“These genes demonstrated significant differences between AD and control samples.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The model's validity was assessed using the top 20 and bottom 10 genes across three biologically distinct AD model systems (68).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Although constrained by input data that lacked non-human information, the AD-specific XGBoost model emphasized the role of infection as a critical factor in AD pathology.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“In this case, XGBoost, a form of extreme gradient boosting (67), was utilized to identify novel genes associated with Alzheimer's disease (68).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“This approach uncovered five previously unassociated gene targets related to immunity in AD: FRRS1 , CTRAM , SCGB3A1 , CIBAR2 , and TMEFF2.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
Experimental — accuracy not yet measured
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The model identified key variables associated with inflammatory processes and oxidative stress.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“By integrating the comprehensive Pharos dataset, a web-based interface for data collected by the Illuminating the Druggable Genome initiative, into XGBoost, an AD-focused model was developed, which included 53 positive (AD-associated) and 3,952 negative (non-AD-associated) genes.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical application of whole-exome sequencing across clinical indications
Retterer, K., Juusola, J., Cho, M. T., Vitazka, P., Millan, F., Gibellini, F., Vertino-Bell, A., Smaoui, N., Neidich, J., Monaghan, K. G., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.148 · Cited by 1,049
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 · doi:10.1016/j.ajhg.2008.09.017 · Cited by 1,034
Self-citationThe Human Phenotype Ontology project: linking molecular biology and disease through phenotype data
Köhler, S., Doelken, S., Mungall, C. J., Bauer, S., Firth, H., Bailleul-Forestier, I., Black, G., Brown, D. L., Brudno, M., Campbell, J., et al. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1026 · Cited by 840
Self-citationResolution of Disease Phenotypes Resulting from Multilocus Genomic Variation
Posey, J. E., Harel, T., Liu, P., Rosenfeld, J. A., James, R. A., Coban‐Akdemir, Z., Walkiewicz, M. A., Bi, W., Xiao, R., Ding, Y., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1516767 · Cited by 772
The Human Phenotype Ontology in 2017
Köhler, S., Vasilevsky, N., Engelstad, M., Foster, E. D., McMurry, J. A., Aymé, S., Baynam, G. S., Bello, S. M., Boerkoel, C. F., Boycott, K. M., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1039 · Cited by 767
Self-citationDeep phenotyping for precision medicine
Robinson, P. N. (2012) · Human Mutation · doi:10.1002/humu.22080 · Cited by 479
Self-citationThe Matchmaker Exchange: A Platform for Rare Disease Gene Discovery
Philippakis, A., Azzariti, D. R., Beltrán, S., Brookes, A. J., Brownstein, C. A., Brudno, M., Brunner, H. G., Buske, O. J., Carey, K., Doll, C., et al. (2015) · Human Mutation · doi:10.1002/humu.22858 · Cited by 476
Self-citationStudy, S. S., Singh, T., Study, I., Study, D., UK10 K Consortium, Kurki, M. I., Curtis, D., Purcell, S. M., Crooks, L., McRae, J., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4267 · Cited by 452
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 · doi:10.1016/j.ajhg.2017.04.003 · Cited by 447
Self-citationMungall, 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 · doi:10.1093/nar/gkw1128 · Cited by 441
Self-citation
Flagged first, then by citations
1 of the 64 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 201–300 of 688
Tingley, K., Lamoureux, M., Pugliese, M., Geraghty, M. T., Kronick, J. B., Potter, B. K., Coyle, D., Wilson, K. R., Kowalski, M., Austin, V., et al. (2020) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-020-01358-z · Cited by 22
Kulmanov, M., & Hoehndorf, R. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008453 · Cited by 22
Novel bi-allelic variants expand the SPTBN4-related genetic and phenotypic spectrum
Buelow, M., Süßmuth, D., Smith, L. D., Aryani, O., Claudia Castiglioni, T., Stenzel, W., Bertini, E., Schuelke, M., & Knierim, E. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00846-5 · Cited by 21
Genetic testing in dementia‐A medical genetics perspective
Huq, A. J., Sexton, A., Lacaze, P. A., Masters, C. L., Storey, E., Velakoulis, D., James, P. A., & Winship, I. (2021) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5535 · Cited by 23
AI-powered drug repurposing for developing COVID-19 treatments
Liu, Z., Chen, X., Carter, W., Moruf, A., Komatsu, T. E., Pahwa, S., Chan‐Tack, K. M., Snyder, K., Petrick, N., Cha, K., et al. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824010-6.00005-8 · Cited by 26
TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction
Marchesin, S., & Silvello, G. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04646-6 · Cited by 21
Functional genomics provide key insights to improve the diagnostic yield of hereditary ataxia
Chen, Z., Tucci, A., Cipriani, V., Gustavsson, E. K., Ibáñez, K., Reynolds, R. H., Zhang, D., Vestito, L., Cisterna‐García, A., Sethi, S., et al. (2023) · Brain · doi:10.1093/brain/awad009 · Cited by 21
Self-citationEcheverry, L. M., Candelo, E., Gomez-Pineda, E., Solís, P., Ramírez, D., Ortiz, D., González, A., Sevillano, X., Cuéllar, J. C., Pachajoa, H., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-33374-x · Cited by 22
Cerebral palsy and genomics: an international consortium
MacLennan, A. H., Kruer, M. C., Baynam, G. S., Moreno-De-Luca, A., Wilson, Y. A., Zhu, C., Wintle, R. F., & Gécz, J. (2018) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.13643 · Cited by 21
Self-citationCLINIFACE: PHENOTYPIC VISUALISATION AND ANALYSIS USING NON-RIGID REGISTRATION OF 3D FACIAL IMAGES
Palmer, R., Helmholz, P., & Baynam, G. S. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2020-301-2020 · Cited by 21
Self-citationMITO-FIND: A study in 390 patients to determine a diagnostic strategy for mitochondrial disease
Kerr, M., Hume, S. L., Omar, F., Koo, D., Barnes, H. K., Khan, M., Aman, S., Wei, X., Alfuhaid, H., McDonald, R. J., et al. (2020) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2020.08.009 · Cited by 21
Expanding Clinical Presentations Due to Variations in THOC2 mRNA Nuclear Export Factor
Kumar, R., Palmer, E. E., Gardner, A. E., Carroll, R., Banka, S., Abdelhadi, O., Donnai, D., Elgersma, Y., Curry, C. J., Gardham, A., et al. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00012 · Cited by 20
Self-citationYubero, D., Benito, D. N., Pijuan, J., Armstrong, J., Martorell, L., Fernández, G., Maynou, J., Jou, C., Roldán, M., Ortez, C., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22084274 · Cited by 21
Fecho, K., Bizon, C., Miller, F. W., Schurman, S. H., Schmitt, C., Xue, W., Morton, K. D., Wang, P., & Tropsha, A. (2021) · JMIR Medical Informatics · doi:10.2196/26714 · Cited by 23
Bowles, B., Ferrer, A., Nishimura, C. J., Pinto e Vairo, F., Rey, T., Leheup, B., Sullivan, J., Schoch, K., Stong, N., Agolini, E., et al. (2021) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.62347 · Cited by 20
Tosco‐Herrera, E., Muñoz‐Barrera, A., Jáspez, D., Rubio‐Rodríguez, L. A., Mendoza‐Alvarez, A., Rodriguez‐Perez, H., Jou, J., Iñigo‐Campos, A., Corrales, A., Ciuffreda, L., et al. (2022) · Human Mutation · doi:10.1002/humu.24459 · Cited by 20
Yu, D., Huang, C.-J., & Tucker, H. O. (2024) · Journal of Developmental Biology · doi:10.3390/jdb12010003 · Cited by 20
Sun, H., Guo, Y., Lan, X., Jia, J., Cai, X., Zhang, G., Xie, J., Liang, Q., Li, Y., & Yu, G. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz930 · Cited by 18
Meng, Z., Wu, J., Zhu, Y., Zhou, W., Fu, C., Liu, X., Liu, S., Ni, M., & Guo, S. (2020) · Chinese Journal of Integrative Medicine · doi:10.1007/s11655-020-2716-4 · Cited by 20
Genomic insights into body size evolution in Carnivora support Peto’s paradox
Huang, X., Sun, D., Wu, T., Liu, X., Xu, S., & Yang, G. (2021) · BMC Genomics · doi:10.1186/s12864-021-07732-w · Cited by 21
Владимирович, М. А., Voskresenskaya, A. A., Ballesta, M. J., Konovalov, F. A., Vasilyeva, T. A., Blanco‐Kelly, F., Pozdeyeva, N. A., Кадышев, В. В., López‐González, V., Guillén‐Navarro, E., et al. (2020) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-020-01484-8 · Cited by 19
Billiet, B., Amati‐Bonneau, P., Desquiret‐Dumas, V., Guehlouz, K., Miléa, D., Gohier, P., Lenaers, G., Mirebeau‐Prunier, D., den Dunnen, J. T., Reynier, P., et al. (2021) · Human Mutation · doi:10.1002/humu.24305 · Cited by 19
HPOFiller: identifying missing protein–phenotype associations by graph convolutional network
Liu, L., Mamitsuka, H., & Zhu, S. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab224 · Cited by 19
VarSight: prioritizing clinically reported variants with binary classification algorithms
Undiagnosed Diseases Network, Holt, J., Wilk, B. M., Birch, C. L., Brown, D. M., Gajapathy, M., Moss, A. C., Sosonkina, N., Wilk, M. A., Anderson, J. A., et al. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3026-8 · Cited by 19
PhenomeXcan: Mapping the genome to the phenome through the transcriptomePreprint
Pividori, M., Rajagopal, P. S., Barbeira, A., Liang, Y., Melia, O., Bastarache, L. A., Park, Y., Wen, X., & Im, H. K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/833210 · Cited by 18
Krenz, H., Gromoll, J., Darde, T., Chalmel, F., Dugas, M., & Tüttelmann, F. (2020) · Human Reproduction · doi:10.1093/humrep/deaa155 · Cited by 18
Kanchanasevee, C., Sriwattanapong, K., Theerapanon, T., Thaweesapphithak, S., Chetruengchai, W., Porntaveetus, T., & Shotelersuk, V. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.573214 · Cited by 18
Zampaglione, E., Maher, M., Place, E., Wagner, N. E., DiTroia, S. P., Chao, K. R., England, E. M., Broad, C. M. G., Catomeris, A. J., Nassiri, S., et al. (2021) · Genetics in Medicine · doi:10.1016/j.gim.2021.09.015 · Cited by 18
Garza-Hernandez, D., Sepúlveda-Villegas, M., Garcia‐Pelaez, J., Aguirre‐Gamboa, R., Lakatos, P. L., Estrada, K., Martinez-Vazquez, M. A., & Treviño, V. (2022) · BMC Genomics · doi:10.1186/s12864-022-08491-y · Cited by 18
Koca, M. B., Nouranı, E., ABBASOĞLU, F., Karadenız, İ., & Sevilgen, F. E. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107755 · Cited by 18
Zhou, W.-Z., Li, W., Shen, H., Wang, W., Chen, W., Zhang, Y., Zeng, Q., Wang, H., Yuan, M., Zeng, Z., et al. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.08.001 · Cited by 18
Genetic and phenotypic spectrum in the NONO ‐associated syndromic disorder
Rößler, F., Beck, A. E., Ball, S., Bartolomaeus, T., Begtrup, A. H., Biskup, S., Caluseriu, O., Delanty, N., Fröhlich, C., Greally, M. T., et al. (2022) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.63044 · Cited by 18
Hotchkiss, J., Manyisa, N. R., Adadey, S. M., Oluwole, O. G., Wonkam‐Tingang, E., Mnika, K., Yalcouyé, A., Nembaware, V., Haendel, M. A., Vasilevsky, N., et al. (2019) · Genes · doi:10.3390/genes10120960 · Cited by 18
Self-citationConsequences of 22q11.2 Microdeletion on the Genome, Individual and Population Levels
Karbarz, M. (2020) · Genes · doi:10.3390/genes11090977 · Cited by 20
DUOX2 variants are a frequent cause of congenital primary hypothyroidism in Thai patients
Sorapipatcharoen, K., Tim‐Aroon, T., Mahachoklertwattana, P., Chantratita, W., Iemwimangsa, N., Sensorn, I., Panthan, B., Jiaranai, P., Noojarern, S., Khlairit, P., et al. (2020) · Endocrine Connections · doi:10.1530/ec-20-0411 · Cited by 18
Bosch, E., Hebebrand, M., Popp, B., Penger, T., Behring, B., Cox, H., Towner, S., Kraus, C. S., Wilson, W. G., Khan, S., et al. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab592 · Cited by 18
Ha, D., Kim, D., Kim, I., Oh, Y., Kong, J., Han, S. K., & Kim, S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac050 · Cited by 20
Design and application of a knowledge network for automatic prioritization of drug mechanisms
Mayers, M., Tu, R., Steinecke, D., Li, T. S., Queralt-Rosiñach, N., & Su, A. I. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac205 · Cited by 18
Boulogne, F., Claus, L. R., Wiersma, H. H., Oelen, R., Schukking, F., de Klein, N., Li, S., Westra, H.-J., van der Zwaag, B., van Reekum, F. E., et al. (2023) · European Journal of Human Genetics · doi:10.1038/s41431-023-01296-x · Cited by 17
Improved characterisation of clinical text through ontology-based vocabulary expansion
Slater, L. T., Bradlow, W. M., Ball, S. T., Hoehndorf, R., & Gkoutos, G. V. (2021) · Journal of Biomedical Semantics · doi:10.1186/s13326-021-00241-5 · Cited by 18
Chromosome 14 deletions, rings, and epilepsy genes: A riddle wrapped in a mystery inside an enigma
Vaisfeld, A., Spartano, S., Gobbi, G., Vezzani, A., & Neri, G. (2020) · Epilepsia · doi:10.1111/epi.16754 · Cited by 17
Gene‐specific facial dysmorphism in Axenfeld‐Rieger syndrome caused by FOXC1 and PITX2 variants
Souzeau, E., Siggs, O. M., Pasutto, F., Knight, L. S. W., Jurado, L. A. P., McGregor, L., Leblanc, S., Barnett, C., Liebelt, J. E., & Craig, J. E. (2020) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.61982 · Cited by 17
Berger, A., Rustemeier, A.-K., Göbel, J., Kadioglu, D., Britz, V., Schubert, K., Mohnike, K. L., Storf, H., & Wagner, T. O. F. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-01831-3 · Cited by 17
Salles, J., Eddiry, S., Lacassagne, E., Laurier, V., Molinas, C., Bieth, É., Franchitto, N., Salles, J.-P., & Tauber, M. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01143-0 · Cited by 17
Gouveia, S., Vázquez-Mosquera, M. E., González‐Vioque, E., Álvarez, J. V., Chans-Gerpe, R., Laranjeira, F., Martins, E., Ferreira, A. C., Ávila-Álvarez, A., & Couce, M. L. (2021) · Genes · doi:10.3390/genes12081262 · Cited by 18
Clinical and genetic characterization of a cohort of 97 CLN6 patients tested at a single center
Rus, C.-M., Weissensteiner, T., Pereira, C. S., Susnea, I., Danquah, B. D., Torres, G. M., Rocha, M. E., Cozma, C., Saravanakumar, D., Mannepalli, S., et al. (2022) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-022-02288-8 · Cited by 17
van Karnebeek, C., O’Donnell‐Luria, A., Baynam, G. S., Baudot, A., Groza, T., Jans, J. J. M., Lassmann, T., Letinturier, M. C. V., Montgomery, S. B., Robinson, P. N., et al. (2024) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-024-03361-0 · Cited by 21
Self-citationThompson, M. D., Knaus, A., Barshop, B. A., Caliebe, A., Muhle, H., Nguyen, T. T. M., Baratang, N., Kinoshita, T., Percy, M. E., Campeau, P. M., et al. (2019) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2019.103822 · Cited by 17
Prot2HG: a database of protein domains mapped to the human genome
Staněk, D., Bis‐Brewer, D. M., Saghira, C., Danzi, M. C., Seeman, P., Laššuthová, P., & Zuchner, S. L. (2020) · Database · doi:10.1093/database/baz161 · Cited by 17
Towards the Understanding of the Human Genome: A Holistic Conceptual Modeling Approach
Alberto García, S., Palacio, A. L., Román, J. F. R., Casamayor, J. C., & Pastor, Ó. (2020) · IEEE Access · doi:10.1109/access.2020.3034793 · Cited by 18
Verstraete, N., Jurman, G., Bertagnolli, G., Ghavasieh, A., Pancaldi, V., & Domenico, M. D. (2020) · Network and Systems Medicine · doi:10.1089/nsm.2020.0011 · Cited by 17
Gholizadeh, E., Karbalaei, R., Khaleghian, A., Salimi, M., Gilany, K., Soliymani, R., Tanoli, Z., Rezadoost, H., Baumann, M., Jafari, M., et al. (2021) · Molecular Pharmacology · doi:10.1124/molpharm.120.000210 · Cited by 18
Vellosillo, P., & Mínguez, P. (2021) · iScience · doi:10.1016/j.isci.2021.102917 · Cited by 17
PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care
Havrilla, J. M., Singaravelu, A., Driscoll, D. M., Minkovsky, L., Helbig, I., Medne, L., Wang, K., Krantz, I. D., & Desai, B. R. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01927-1 · Cited by 15
Revised JNLPBA Corpus: A Revised Version of Biomedical NER Corpus for Relation Extraction TaskPreprint
Huang, M.-S., Lai, P., Tsai, R. T., & Hsu, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10219 · Cited by 17
Althagafi, A., Alsubaie, L., Kathiresan, N., Mineta, K., Aloraini, T., Mutairi, F. A., Alfadhel, M., Gojobori, T., Alfares, A. A., & Hoehndorf, R. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab859 · Cited by 16
Genetic Risk Factors for Early-Onset Merkel Cell Carcinoma
Mohsin, N., Hunt, D., Yan, J., Jabbour, A. J., Nghiem, P., Choi, J., Zhang, Y., Freeman, A. F., Bergerson, J., Dell’Orso, S., et al. (2024) · JAMA Dermatology · doi:10.1001/jamadermatol.2023.5362 · Cited by 17
Self-citationBraun, J. M., Kalloo, G., Kingsley, S. L., & Li, N. (2019) · Environment International · doi:10.1016/j.envint.2019.05.071 · Cited by 17
Giannoula, A., Centeno, E., Mayer, M. Á., Sanz, F., & Furlong, L. I. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa964 · Cited by 16
‘There and Back Again’—Forward Genetics and Reverse Phenotyping in Pulmonary Arterial Hypertension
Swietlik, E. M., Prapa, M., Martin, J. M., Pandya, D., Auckland, K., Morrell, N. W., & Gräf, S. (2020) · Genes · doi:10.3390/genes11121408 · Cited by 15
Mak, B. C., Russo, R. S., Gambello, M. J., Fleischer, N., Black, E. D., Leslie, E. J., Murphy, M. M., The Emory 3q29 Project, & Mulle, J. G. (2021) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.62227 · Cited by 15
PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning
Chen, Z., Zheng, Y., Yang, Y., Huang, Y., Zhao, S., Zhao, H., Yu, C., Dong, X., Zhang, Y., Wang, L., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.12.008 · Cited by 16
Why do we keep ignoring sex in kidney disease?
Jankowska, M., Soler, M. J., Stevens, K., & Torrá, R. (2023) · Clinical Kidney Journal · doi:10.1093/ckj/sfad183 · Cited by 17
Contribution of model organism phenotypes to the computational identification of human disease genes
Alghamdi, S. M., Schofield, P. N., & Hoehndorf, R. (2022) · Disease Models & Mechanisms · doi:10.1242/dmm.049441 · Cited by 15
Text Mining for Building Biomedical Networks Using Cancer as a Case Study
Conceição, S. I. R., & Couto, F. M. (2021) · Biomolecules · doi:10.3390/biom11101430 · Cited by 15
Mohammadi, P., Siavashani, E. S., Mohammadi, M. F., Bahramy, A., Almadani, N., & Garshasbi, M. (2021) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.1834 · Cited by 15
Aguiar, T. F. M., Teixeira, A. C. B., Scliar, M. O., de Barros, J. S., Lemes, R. B., Souza, S., Tolezano, G. C., dos Santos, F. A., Tojal, I., Cypriano, M., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.858396 · Cited by 16
Applying knowledge-driven mechanistic inference to toxicogenomics
Tripodi, I. J., Callahan, T. J., Westfall, J., Meitzer, N. S., Dowell, R. D., & Hunter, L. (2020) · Toxicology in Vitro · doi:10.1016/j.tiv.2020.104877 · Cited by 15
The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicinePreprint
Jacobsen, J. O. B., Baudis, M., Baynam, G. S., Beckmann, J. S., Beltrán, S., Callahan, T. J., Chute, C. G., Courtot, M., Daniš, D., Elemento, O., et al. (2021) · medRxiv · doi:10.1101/2021.11.27.21266944 · Cited by 15
Self-citationDisease ontologies for knowledge graphs
Kurbatova, N., & Swiers, R. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04173-w · Cited by 15
Exome Sequencing of 5 Families with Severe Early-Onset Periodontitis
Richter, G. M., Wagner, G., Reichenmiller, K., Staufenbiel, I., Martins, O., Löscher, B.-S., Holtgrewe, M., Jepsen, S. V., Dommisch, H., & Schäefer, A. S. (2021) · Journal of Dental Research · doi:10.1177/00220345211029266 · Cited by 14
Bosch, E., Popp, B., Güse, E., Skinner, C. D., van der Sluijs, P. J., Maystadt, I., Pinto, A. M., Renieri, A., Bruno, L. P., Granata, S., et al. (2023) · Genetics in Medicine · doi:10.1016/j.gim.2023.100950 · Cited by 14
FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology
Groza, T., Gration, D., Baynam, G. S., & Robinson, P. N. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae406 · Cited by 14
Self-citationClinical Bioinformatics in Precise Diagnosis of Mitochondrial Disease
Shen, L., McCormick, E. M., Muraresku, C. C., Falk, M. J., & Gai, X. (2020) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2020.02.002 · Cited by 15
Ferreira, J. P., Lamiral, Z., Xhaard, C., Duarte, K., Bresso, E., Devignes, M., Floch, É. L., Dandine‐Roulland, C., Deleuze, J., Wagner, S., et al. (2020) · European Journal of Endocrinology · doi:10.1530/eje-20-0246 · Cited by 15
The Use of AI for Phenotype-Genotype Mapping
Sharma, J., & Goel, P. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4690-8_21 · Cited by 13
Zhu, C., Xia, X., Li, N., Zhong, F., Yang, Z., & Liu, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107262 · Cited by 15
Graph databases in systems biology: a systematic review
Mazein, I., Rougny, A., Mazein, A., Henkel, R., Gütebier, L., Michaelis, L., Ostaszewski, M., Schneider, R., Satagopam, V., Jensen, L. J., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae561 · Cited by 15
Prioritizing Pain-Associated Targets with Machine Learning
Jeon, M., Jagodnik, K. M., Kropiwnicki, E., Stein, D. J., & Ma’ayan, A. (2021) · Biochemistry · doi:10.1021/acs.biochem.0c00930 · Cited by 13
Common data elements to standardize genomics studies in cerebral palsy
Wilson, Y. A., Smithers‐Sheedy, H., Ostojic, K., Waight, E., Kruer, M. C., Fahey, M. C., ICPGC Phenotype Working Group*, Baynam, G. S., Gécz, J., Badawi, N. A., et al. (2022) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.15245 · Cited by 12
Self-citationMeasuring the importance of vertices in the weighted human disease network
Almasi, S. M., & Hu, T. (2019) · PLoS ONE · doi:10.1371/journal.pone.0205936 · Cited by 13
Diagnostics of Hereditary Connective Tissue Disorders by Genetic Next-Generation Sequencing
Pope, M. K., Ratajska, A., Johnsen, H., Rypdal, K. B., Sejersted, Y., & Paus, B. (2019) · Genetic Testing and Molecular Biomarkers · doi:10.1089/gtmb.2019.0064 · Cited by 13
Disrupted minor intron splicing is prevalent in Mendelian disorders
Olthof, A. M., Rasmussen, J. S., Campeau, P. M., & Kanadia, R. (2020) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.1374 · Cited by 13
Vela-Amieva, M. B., Alcántara‐Ortigoza, M. A., Angel, A. G., Belmont‐Martínez, L., López-Candiani, C., & Ibarra‐González, I. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-01693-9 · Cited by 14
Cherian, C., Appendino, J. P., Ashtiani, S., Federico, P., Molnar, C., Kerr, M., Khan, A., Au, P. Y. B., & Klein, K. M. (2021) · Journal of Neurology · doi:10.1007/s00415-021-10808-y · Cited by 13
Predicting genes from phenotypes using human phenotype ontology (HPO) terms
Slavotinek, A. M., Prasad, H., Yip, T., Rego, S. M., Hoban, H., & Kvale, M. N. (2022) · Human Genetics · doi:10.1007/s00439-022-02449-6 · Cited by 14
Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View
Ranea, J. A. G., Perkins, J. R., Chagoyen, M., Díaz-Santiago, E., & Pazos, F. (2022) · Genes · doi:10.3390/genes13061081 · Cited by 14
OARD: Open annotations for rare diseases and their phenotypes based on real-world data
Liu, C., Ta, C., Havrilla, J. M., Nestor, J. G., Spotnitz, H. M., Geneslaw, A. S., Hu, Y., Chung, W. K., Wang, K., & Weng, C. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.08.002 · Cited by 12
Carmody, L., Gargano, M. A., Toro, S., Vasilevsky, N., Adam, M. P., Blau, H., Chan, L., Gómez‐Andrés, D., Horváth, R., Kraus, M. L., et al. (2023) · Med · doi:10.1016/j.medj.2023.10.003 · Cited by 12
Self-citationKnowledge graph‐driven data processing for business intelligence
Dey, L. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1529 · Cited by 12
Yusuff, T., Jensen, M., Yennawar, S., Pizzo, L., Karthikeyan, S., Gould, D. J., Sarker, A., Gedvilaite, E., Matsui, Y., Iyer, J. S., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008792 · Cited by 12
Tarlinton, R. E., Fabijan, J., Hemmatzadeh, F., Meers, J., Owen, H. C., Sarker, N., Seddon, J. M., Simmons, G., Speight, N., Trott, D. J., et al. (2021) · Conservation Genetics · doi:10.1007/s10592-021-01340-7 · Cited by 12
GPCards: An integrated database of genotype–phenotype correlations in human genetic diseases
Li, B., Wang, Z., Chen, Q., Li, K., Wang, X., Wang, Y., Zeng, Q., Han, Y., Lü, B., Zhao, Y., et al. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.011 · Cited by 11
Savojardo, C., Babbi, G., Martelli, P. L., & Casadio, R. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.617016 · Cited by 15
The role of genomics and genetics in pulmonary arterial hypertension
Swietlik, E. M., Gräf, S., & Morrell, N. W. (2020) · Global Cardiology Science and Practice · doi:10.21542/gcsp.2020.13 · Cited by 14
Activity-Dependent Mitochondrial Plasticity and Mitoproteome are Dysregulated in Fragile X Syndrome
Bülow, P., Zlatic, S. A., Wenner, P., Bassell, G. J., & Faúndez, V. (2020) · Molecular Brain · doi:10.1186/s13041-021-00783-w · Cited by 13
100,000 genomes pilot on rare-disease diagnosis in health care – preliminary report
The Genomes-Project-Pilot-Investigators (2022) · Yearbook of pediatric endocrinology · doi:10.1530/ey.19.15.16 · Cited by 11
Specific phenotype semantics facilitate gene prioritization in clinical exome sequencing
Tomar, S., Sethi, R., & Lai, P. S. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-019-0412-7 · Cited by 11
Structured reviews for data and knowledge-driven research
Queralt-Rosiñach, N., Stupp, G. S., Li, T. S., Mayers, M., Hoatlin, M. E., Might, M., Good, B. M., & Su, A. I. (2020) · Database · doi:10.1093/database/baaa015 · Cited by 11
Boschen, K. E., Ptacek, T., Simon, J. M., & Parnell, S. E. (2020) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14389 · Cited by 11
Metadata
- Authors
- 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., Osumi-Sutherland, D., Cipriani, V., Balhoff, J. P., Conlin, T., Blau, H., Baynam, G., Palmer, R., Gratian, D., Dawkins, H., Segal, M., Jansen, A. C., Muaz, A., Chang, W. H., Bergerson, J., Laulederkind, S. J. F., Yüksel, Z., Beltran, S., Freeman, A. F., Sergouniotis, P. I., Durkin, D., Storm, A. L., Hanauer, M., Brudno, M., Bello, S. M., Sincan, M., Rageth, K., Wheeler, M. T., Oegema, R., Lourghi, H., Della Rocca, M. G., Thompson, R., Castellanos, F., Priest, J., Cunningham-Rundles, C., Hegde, A., Lovering, R. C., Hajek, C., Olry, A., Notarangelo, L., Similuk, M., Zhang, X. A., Gómez-Andrés, D., Lochmüller, H., Dollfus, H., Rosenzweig, S., Marwaha, S., Rath, A., Sullivan, K., Smith, C., Milner, J. D., Leroux, D., Boerkoel, C. F., Klion, A., Carter, M. C., Groza, T., Smedley, D., Haendel, M. A., Mungall, C., & Robinson, P. N.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- The Jackson Laboratory for Genomic Medicine (US)
- Oregon Health & Science University (US)
- Queen Mary University of London (GB)
- Monarch Initiative, monarchinitiative.org
- Lawrence Berkeley National Laboratory (US)
- European Bioinformatics Institute (GB)
- Oregon State University (US)
- University of North Carolina at Chapel Hill (US)
- The Kids Research Institute Australia (AU)
- Curtin University (AU)
- Government of Western Australia (AU)
- SimulConsult (US)
- Vrije Universiteit Brussel (BE)
- Garvan Institute of Medical Research (AU)
- University of Toronto (CA)
- National Institutes of Health (US)
- Marquette University (US)
- Bioscientia GmbH, Ingelheim, Germany
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain
- Manchester Royal Eye Hospital (GB)
- Inserm (FR)
- Jackson Laboratory (US)
- Sanford Health (US)
- Stanford Medicine (US)
- University Medical Center Utrecht (NL)
- ICF, Rockville, MD, USA
- Newcastle University (GB)
- Mount Sinai School of Medicine, New York, NY, USA
- University College London (GB)
- Child Neurology Unit. Hospital Universitari Vall d’Hebron, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain
- University of Ottawa (CA)
- Hôpitaux Universitaires de Strasbourg (FR)
- Children's Hospital of Philadelphia (US)
- University of Connecticut (US)
- Corresponding author
- Robinson, Peter N
- ORCID
- Köhler, Sebastian
- Carmody, Leigh
- Vasilevsky, Nicole
- Jacobsen, Julius O B
- Gourdine, Jean-Philippe
- Gargano, Michael
- Harris, Nomi L
- Matentzoglu, Nicolas
- McMurry, Julie A
- Osumi-Sutherland, David
- Cipriani, Valentina
- Balhoff, James P
- Conlin, Tom
- Blau, Hannah
- Baynam, Gareth
- Palmer, Richard
- Segal, Michael
- Jansen, Anna C
- Bergerson, Jenna
- Laulederkind, Stanley J F
- Yüksel, Zafer
- Beltran, Sergi
- Freeman, Alexandra F
- Sergouniotis, Panagiotis I
- Storm, Andrea L
- Hanauer, Marc
- Brudno, Michael
- Bello, Susan M
- Sincan, Murat
- Rageth, Kayli
- Wheeler, Matthew T
- Oegema, Renske
- Thompson, Rachel
- Castellanos, Francisco
- Priest, James
- Lovering, Ruth C
- Hajek, Catherine
- Olry, Annie
- Notarangelo, Luigi
- Similuk, Morgan
- Zhang, Xingmin A
- Lochmüller, Hanns
- Dollfus, Hélène
- Rosenzweig, Sergio
- Marwaha, Shruti
- Rath, Ana
- Sullivan, Kathleen
- Smith, Cynthia
- Milner, Joshua D
- Boerkoel, Cornelius F
- Klion, Amy
- Carter, Melody C
- Groza, Tudor
- Smedley, Damian
- Haendel, Melissa A
- Mungall, Chris
- Robinson, Peter N
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-22
- Volume
- 47
- Issue
- D1
- Pages
- D1018-D1027
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6324074
- OpenAlex
- W2901332105
- Cited by
- 677
- References
- 64
- Citations by year
- 2026: 27 · 2025: 49 · 2024: 57 · 2023: 68 · 2022: 108 · 2021: 157 · 2020: 134 · 2019: 72 · 2018: 2
- FWCI
- 33.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- Murdoch University Research Portal (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion, other-oa)
- HAL (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eSpace (Curtin University) (submittedVersion, cc-by)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- National Institutes of Health (OD #5R24OD011883, 1 OT3 OD02464-01 UNCCH)
- Forums for Integrative Phenomics (U13 CA221044-01)
- NCATS Data Translator (1OT3TR002019)
- NCATS National Center for Digital Health Informatics Innovation (U24 TR002306)
- British Heart Foundation (RG/13/5/30112)
- Division of Intramural Research NIAID (01GM1608)
- European Union ’s Horizon 2020 Research and Innovation Programme (779257)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- Human Phenotype Ontology, phenotypic abnormalities, rare diseases, deep phenotyping, disease definition, ontology interoperability, Exomiser, variant interpretation, ontology mapping, clinical features, human genes
- MeSH terms
- Congenital Abnormalities, Whole Genome Sequencing, Humans, Phenotype, Genetic Variation, Computational Biology, Genetic Predisposition to Disease, Internet, Databases, Genetic, Rare Diseases, Knowledge Bases, Biological Ontologies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
