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 401–500 of 688
Domain specific semantic categories in biomedical applications
Ayams, J. N., Okunlola, F. O., Chimbekujwo, K. I., Taiwo, D. I., Oyewole, O. A., Adetunji, C. O., Adetuyi, B. O., Chinazaekpele, C. C., Adetuyi, T. G., Okunlola, A. R., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15452-2.00029-7 · Cited by 7
Yoon, J. G., Jang, H., Lee, S., Jang, S. S., Park, S., Cho, J., Kim, M., Han, J., Yun, H., Kim, M. J., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01513-w · Cited by 5
Kalinichenko, A., Huemer, J., Humer, T., Haimel, M., Svatoň, M., Socquet-Juglard, N., Casoni, G. P., Prakash, C., von der Linde, M., Pázmándi, J., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.ado3825 · Cited by 5
Outlier concepts auditing methodology for a large family of biomedical ontologies
Zheng, L., Min, H., Chen, Y., Keloth, V. K., Geller, J., Perl, Y., & Hripcsak, G. M. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01311-x · Cited by 5
Nédellec, C., Sauvion, C., Bossy, R., Borovikova, M., & Deléger, L. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305475 · Cited by 5
Kim, I. B., Kim, M.-H., Jung, S., Kim, W. K., Lee, J., Ju, Y. S., Webster, M. J., Kim, J. H., Kim, J., Kim, H. J., et al. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01284-1 · Cited by 5
Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes
Chang, W. H., Mashouri, P., Lozano, A. X., Johnstone, B., Husić, M., Olry, A., Maiella, S., Balcı, T. B., Sawyer, S. L., Robinson, P. N., et al. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-0812-7 · Cited by 5
Self-citationPredicting disease‐associated genes: Computational methods, databases, and evaluations
Luo, P., Chen, B., Liao, B., & Wu, F. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1383 · Cited by 5
Gordon syndrome: Dental implications and a case report
Roomaney, I. A., Walters, J., Spencer, C., & Chetty, M. (2021) · Special Care in Dentistry · doi:10.1111/scd.12615 · Cited by 5
Shahri, M. P., & Kahanda, I. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04421-z · Cited by 5
Mitochondrial disease manifestations in relation to transcriptome location and function
Jakkamsetti, V., Balasubramaniam, S. R., Grover, N., & Pascual, J. M. (2021) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2021.12.008 · Cited by 5
Identification and quantification of oligogenic loss-of-function disorders
Stefanski, A., Pérez‐Palma, E., Mrdjen, M., McHugh, M., Leu, C., & Lal, D. (2021) · Genetics in Medicine · doi:10.1016/j.gim.2021.10.026 · Cited by 5
Manzini, E., Garrido-Aguirre, J., Fonollosa, J., & Perera-Lluna, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117446 · Cited by 8
George‐Sankoh, I., MacMullen, L. E., Chinwalla, A., Taylor, D. M., Ganetzky, R. D., Stanley, K. D., McCormick, E. M., Zolkipli‐Cunningham, Z., & Falk, M. J. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae134 · Cited by 5
Davidović, R., Perović, V., Gemović, B., & Veljković, N. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz836 · Cited by 5
Piergiorge, R. M., de Vasconcelos, A. T. R., Pimentel, M. M. G., & Santos-Rebouças, C. B. (2020) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2020.1821916 · Cited by 5
Zheng, S., Huang, H., Ma, L., & Zhu, T. (2022) · BMC Medical Genomics · doi:10.1186/s12920-022-01336-3 · Cited by 6
Eberhardt, R. Y., Wright, C. F., FitzPatrick, D. R., Hurles, M. E., & Firth, H. V. (2023) · Genetics in Medicine Open · doi:10.1016/j.gimo.2023.100836 · Cited by 5
PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies
Yao, X., Ouyang, S., Lian, Y., Peng, Q., Zhou, X., Huang, F., Hu, X., Shi, F., & Xia, J. (2024) · Genome Medicine · doi:10.1186/s13073-024-01330-7 · Cited by 5
Mubungu, G. L., Lumaka, A. Z., Mvuama, N. M., Tshika, D., Makay, P. B., Lukusa-Tshilobo, P., & Devriendt, K. (2020) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.61477 · Cited by 5
van der Velde, K. J., van den Hoek, S., van Dijk, F., Hendriksen, D., van Diemen, C. C., Johansson, L. F., Abbott, K. M., Deelen, P., Sikkema‐Raddatz, B., & Swertz, M. A. (2020) · Advanced Genetics · doi:10.1002/ggn2.10023 · Cited by 4
Phexpo: a package for bidirectional enrichment analysis of phenotypes and chemicals
Hawthorne, C., Simpson, D. A. C., Devereux, B. J., & López–Campos, G. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa023 · Cited by 5
Владимирович, М. А., Mishina, I. A., Кадышев, В. В., Repina, S. A., Шурыгина, М. Ф., Shchagina, O., Vasserman, N. N., Vasilyeva, T. A., Kutsev, S. I., & Зинченко, Р. А. (2020) · BMC Medical Genetics · doi:10.1186/s12881-020-01093-z · Cited by 5
Clinical Decision Support Systems
Li, J., Yu, T., & Zhou, T. (2024) · Innovative Medical Devices. · doi:10.1007/978-981-97-9551-2_5 · Cited by 5
Attitudes Toward Deaf People on Providence Island.
Woodward, J. (1978) · Sign language studies · Cited by 5
Schilder, B. M., Murphy, K. B., Dash, H., Zhang, Y., Gordon-Smith, R., Chapman, J., Otani, M., & Skene, N. (2023) · medRxiv · doi:10.1101/2023.02.13.23285820 · Cited by 5
Godínez-Zamora, G. F., Baeza-Capetillo, P., Saucedo-Ramírez, O. J., Del-Rio-Navarro, B. E., Espinosa‐Padilla, S. E., Morán-Barroso, V. F., & Aguirre‐Hernández, J. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1638544 · Cited by 4
Johnatty, S. E., Tudini, E., Parsons, M. T., Michailidou, K., Zanti, M., Canson, D. M., Davidson, A. L., Berger, T. R., Rosti, R. Ö., Kratz, C. P., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.10.007 · Cited by 4
Information content as a health system screening tool for rare diseases
Groza, T., Robinson, P. N., Lim, W. K., Narasimhalu, K., Hsieh, J. Y. C., Yeo, K. K., Goh, B. K., Thomas, T. G. S., Wong, T. Y., Karnani, N., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02096-x · Cited by 4
Self-citationGroza, T., Marcello, A., Carlisle, T., Lim, W. K., Haendel, M. A., Karnani, N., Robinson, P. N., Graeßner, H., Chong, J. X., Baynam, G. S., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100558 · Cited by 5
Self-citationSOULEIMAN, I. A., & Souleiman, H. A. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100365 · Cited by 4
Advancing the allergenicity assessment of new proteins using a text mining resource
Novoa, J. A. R., Fernández‐Dumont, A., Mills, E. N. C., Moreno, F. J., & Pazos, F. (2024) · Food and Chemical Toxicology · doi:10.1016/j.fct.2024.114638 · Cited by 4
THE FIGHT INHERITED RETINAL BLINDNESS! PROJECT
Simunovic, M. P., Moore, A. T., Grigg, J., Sergouniotis, P. I., Mahroo, O. A., Vincent, A. L., Singh, M. S., Fischer, M. D., Edwards, T. L., Mack, H. G., et al. (2024) · Retina · doi:10.1097/iae.0000000000004296 · Cited by 4
Self-citationKafkas, Ş., Abdelhakim, M., Uludağ, M., Althagafi, A., Alghamdi, M. A., & Hoehndorf, R. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05406-w · Cited by 4
Ahmad, N., Fazeli, W., Schließke, S., Lesca, G., Gokce‐Samar, Z., Mekbib, K. Y., Jin, S. C., Burton, J. E., Hoganson, G. E., Petersen, A. K., et al. (2023) · Pediatric Neurology · doi:10.1016/j.pediatrneurol.2023.08.023 · Cited by 4
Kumar, D. (2023) · Journal of Genetics · doi:10.1007/s12041-023-01444-8 · Cited by 4
Semantics-enabled biomedical literature analytics
Halil Kilicoglu, B., Ensan, F., McInnes, B. T., & Wang, L. L. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104588 · Cited by 4
Vega-Hanna, L., Casas‐Alba, D., Balsells, S., Bolasell, M., Rubio, P., García‐García, A., García-García, O., O’Callaghan, M., Pascual‐Alonso, A., Armstrong, J., et al. (2024) · Diagnostics · doi:10.3390/diagnostics15010010 · Cited by 4
Incorporating Multi-Scale Module Kernel for Disease-Gene Identification in Biological Networks
Xiang, J., Zeng, K., Chen, S., Meng, X., Zheng, R., Zheng, Y., Long, Y., & Li, M. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010088 · Cited by 4
Rim, J. H., Choi, Y. J., & Gee, H. Y. (2020) · Biomolecules · doi:10.3390/biom10030449 · Cited by 4
Zebrafish modeling mimics developmental phenotype of patients with RAPGEF1 mutation
Li, N., Zhou, P., Yang, M., Fang, X., Krämer, N., Mughal, T. A., Abbasi, A. A., Yang, Y., Kaindl, A. M., & Hu, H. (2021) · Clinical Genetics · doi:10.1111/cge.13965 · Cited by 4
Pendleton, S. C., Slater, L. T., Karwath, A., Gilbert, R. M., Davis, N., Pesudovs, K., Liu, X., Denniston, A. K., Gkoutos, G. V., & Braithwaite, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104542 · Cited by 4
A Systematic Study of Intelligent Face Scanning in Rare Disease Detection
Sharma, S., & Malhotra, D. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-16-6332-1_39 · Cited by 4
AnthraxKP: a knowledge graph-based, Anthrax Knowledge Portal mined from biomedical literature
Feng, B., & Gao, J. (2022) · Database · doi:10.1093/database/baac037 · Cited by 4
Improving variant prioritization in exome analysis by entropy‐weighted ensemble of multiple tools
Fan, Y., Zhou, Y., Liu, H., Luo, X., Xu, T., Sun, Y., Yang, T., Chen, L., Gu, X., & Yu, Y. (2022) · Clinical Genetics · doi:10.1111/cge.14257 · Cited by 4
Lubala, T. K., Kayembe‐Kitenge, T., Mubungu, G. L., Lumaka, A. Z., Kanteng, G. W., Savage, S., Luboya, O. N., Hagerman, R. J., Devriendt, K., & Lukusa-Tshilobo, P. (2023) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2023.104819 · Cited by 4
Mechanisms of cone sensitivity loss in retinitis pigmentosa
Simunovic, M. P., & Mammo, Z. N. (2024) · Ophthalmic and Physiological Optics · doi:10.1111/opo.13280 · Cited by 4
Ossio, R., Garcia-Salinas, O. I., Anaya-Mancilla, D. S., García-Sotelo, J. S., Aguilar, L., Adams, D. J., & Robles‐Espinoza, C. D. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz458 · Cited by 4
Sánchez, J. A., Gil-Martínez, A. L., Cisterna‐García, A., García-Ruiz, S., Gómez-Pascual, A., Reynolds, R. H., Nalls, M. A., Hardy, J., Ryten, M., & Botía, J. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab175 · Cited by 4
Dementia and cognitive decline
Huq, A. J., Eratne, D., & Velakoulis, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99417-0.00006-9 · Cited by 4
Phenotypic, functional and taxonomic features predict host-pathogen interactionsPreprint
Liu-Wei, W., Kafkas, Ş., & Hoehndorf, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/508762 · Cited by 4
The Regulatory Evolution of the Primate Fine-Motor SystemPreprint
Wirthlin, M. E., Kaplow, I. M., Lawler, A. J., He, J., Phan, B. N., Brown, A. R., Stauffer, W. R., & Pfenning, A. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.27.356733 · Cited by 4
Chen, J., Landback, P., Arsala, D., Guzzetta, A., Xia, S., Atlas, J., Sosa, D., Zhang, Y. E., Cheng, J., Shen, B., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.14.567139 · Cited by 4
Kvamme, J., Badsha, M. B., Martin, E. A., Wu, J., Wang, X., & Fu, A. Q. (2025) · Genetics · doi:10.1093/genetics/iyaf064 · Cited by 3
Achkasova, K. A., Subbotin, P. V., Zhukov, V., Filat’eva, A. E., Tarabykin, V. S., & Kondakova, E. V. (2026) · Cells · doi:10.3390/cells15060537 · Cited by 5
Alkanhal, A., Ducas, R., Mackie, A. S., Seaman, C. S., Averin, K., Mah, K., & Khoury, M. (2022) · Pediatric Cardiology · doi:10.1007/s00246-022-03073-x · Cited by 3
Germline genetic variants in a case of familial cancer: RAD51D and four other co-segregated variants
Biswas, S., Manekar, S. C., Kantharia, S., & Bakshi, S. R. (2024) · Journal of Genetics · doi:10.1007/s12041-024-01481-x · Cited by 3
Heterogeneous network impulsive dynamics for identifying disease-associated genes
Xiang, J., Chen, S., Wang, L., Meng, X., & Li, M. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822257 · Cited by 3
Dukda, S., Kumar, M., Calcino, A., Schmitz, U., & Field, M. A. (2025) · Human Genomics · doi:10.1186/s40246-025-00811-z · Cited by 5
Ciaccio, C., Pantaleoni, C., Bulgheroni, S., Sciacca, F. L., & D’Arrigo, S. (2020) · The Cerebellum · doi:10.1007/s12311-020-01145-3 · Cited by 3
Choosing preferable labels for the Japanese translation of the Human Phenotype Ontology
Ninomiya, K., Takatsuki, T., Kushida, T., Yamamoto, Y., & Ogishima, S. (2020) · Genomics & Informatics · doi:10.5808/gi.2020.18.2.e23 · Cited by 3
Konopka, T., Ng, S. L.-K., & Smedley, D. P. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009283 · Cited by 3
Self-citationA meta-analysis of prognostic biomarkers in neonatal retinal hemorrhage
Xu, Y., Wang, Y., & Li, S. (2021) · International Ophthalmology · doi:10.1007/s10792-021-02055-x · Cited by 3
Giovannetti, A., Bianco, S. D., Traversa, A., Panzironi, N., Bruselles, A., Lazzari, S., Liorni, N., Tartaglia, M., Carella, M., Pizzuti, A., et al. (2022) · Human Mutation · doi:10.1002/humu.24399 · Cited by 3
Developmental and Epileptic Encephalopathy 76: Case Report and Review of Literature
Han, X., Deng, J., Chen, C., Wang, X., Fang, F., Li, H., Luo, J., & Wu, J. (2022) · Children · doi:10.3390/children9121967 · Cited by 3
Petrova, N. V., Tebieva, I. S., Кадышев, В. В., Getoeva, Z., Balinova, N., Владимирович, М. А., Vasilyeva, T. A., Ginter, E. K., Kutsev, S. I., & Зинченко, Р. А. (2023) · PeerJ · doi:10.7717/peerj.14514 · Cited by 3
Design of Multiple Ontology Based Agro Knowledge Mining Model
Abdullah, A. B., Murali, E., Sreeji, S., Balusamy, B., & Rajashree, S. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i7.7829 · Cited by 3
Novel Variants Linked to the Prodromal Stage of Parkinson’s Disease (PD) Patients
Badawy, M. T., Salama, A. A., & Salama, M. (2024) · Diagnostics · doi:10.3390/diagnostics14090929 · Cited by 2
Deciphering the Complexity of FSHD: A Multimodal Approach as a Model for Rare Disorders
Megalizzi, D., Trastulli, G., Colantoni, L., Piorgo, E. P., Primiano, G., Sancricca, C., Caltagirone, C., Cascella, R., Strafella, C., & Giardina, E. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252010949 · Cited by 4
A representation model for biological entities by fusing structured axioms with unstructured texts
Lou, P., Dong, Y., Yepes, A. J., & Li, C. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa913 · Cited by 3
Poon, C.-L., & Chen, C.-Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.696836 · Cited by 3
Improving accessibility and distinction between negative results in biomedical relation extraction
Sousa, D., Lamúrias, A., & Couto, F. M. (2020) · Genomics & Informatics · doi:10.5808/gi.2020.18.2.e20 · Cited by 3
Sriwattanapong, K., Rojnueangnit, K., Theerapanon, T., Srichomthong, C., Porntaveetus, T., & Shotelersuk, V. (2021) · International Journal of Neonatal Screening · doi:10.3390/ijns7010009 · Cited by 3
Pfeffer, T. J., List, M., Schippert, C., Auber, B., Ricke‐Hoch, M., Moulig, V. A., Berliner, D., Bauersachs, J. K., & Hilfiker‐Kleiner, D. (2022) · Clinical Research in Cardiology · doi:10.1007/s00392-022-02034-x · Cited by 3
Aitken, S., Firth, H. V., Wright, C. F., Hurles, M. E., FitzPatrick, D. R., & Semple, C. A. M. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100162 · Cited by 2
Varstation: a Complete and Efficient Tool to Support NGS Data Analysis
Cervato, M. C. (2021) · Bioinformatics & Proteomics Open Access Journal · doi:10.23880/bpoj-16000145 · Cited by 3
Dumas, G., Malesys, S., & Bourgeron, T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/658658 · Cited by 3
D4: Deep Drug-drug interaction Discovery and DemystificationPreprint
Noor, A., Liu-Wei, W., Barnawi, A., Nour, R., Assiri, A. A., Bukhari, S. A. C., & Hoehndorf, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.08.032011 · Cited by 3
Hartsfield, J. K., Gudsoorkar, P., & Morford, L. A. (2021) · doi:10.1002/9781119608912.ch13 · Cited by 3
Realization of Network Security Knowledge Base with Knowledge Graph
Ouyang, Z., Li, X., Wang, B., & Ji, Z. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-1698-5_6 · Cited by 2
Groza, T., Rayabsri, W., Gration, D., Hariram, H., Jamuar, S. S., & Baynam, G. S. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.03.014 · Cited by 2
Self-citationRankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases
Zhang, Y., Ahsan, M. U., Wang, P., Lin, X., Campbell, I. M., Liu, C., Chung, W. K., Weng, C., & Wang, K. (2026) · Genome Medicine · doi:10.1186/s13073-026-01701-2 · Cited by 1
An Integrative Disease Information Network Approach to Similar Disease Detection
Xu, W., Duan, L., Zheng, H., Li‐Ling, J., Jiang, W., Zhang, Y., Wang, T., & Qin, R. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3110127 · Cited by 2
Konopka, T., Vestito, L., & Smedley, D. P. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab026 · Cited by 2
Self-citationSilva, J. D. D., Teles, N. O., Tkachenko, N., Fino, J., Marques, M., Fortuna, A. M., & David, D. (2022) · Biomedicines · doi:10.3390/biomedicines11010012 · Cited by 2
Зинченко, Р. А., Tebieva, I. S., Gabisova, Y. V., Шукан, Е. Ю., Khokhova, A., Владимирович, М. А., & Kutsev, S. I. (2024) · Bulletin of Russian State Medical University · doi:10.24075/brsmu.2024.025 · Cited by 2
Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization
Beckwith, M. A., Daniš, D., Bridges, Y., Jacobsen, J. O. B., Smedley, D. P., & Robinson, P. N. (2024) · Genetics in Medicine · doi:10.1016/j.gim.2024.101292 · Cited by 1
Self-citationTran, M. N., Jun, H. J., & Lee, S. (2024) · Medicine · doi:10.1097/md.0000000000040717 · Cited by 2
Aspromonte, M. C., Conte, A. D., Zhu, S., Tan, W., Shen, Y., Zhang, Y., Li, Q., Wang, M. H., Babbi, G., Bovo, S., et al. (2025) · Human Genetics · doi:10.1007/s00439-024-02722-w · Cited by 2
Health care cube integrator for health care databases
Trivedi, S. A., Patel, M., & Patel, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822468-7.00013-4 · Cited by 2
Data Analysis in Rare Disease Diagnostics
Veeramachaneni, V. (2020) · Journal of the Indian Institute of Science · doi:10.1007/s41745-020-00189-y · Cited by 2
Actionable and incidental neuroradiological findings in twins with neurodevelopmental disorders
Myers, L., Ho, M., Cauvet, É., Remnélius, K. L., Carlsson, T., Kuja‐Halkola, R., Tammimies, K., & Bölte, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-79959-8 · Cited by 2
Femoral artery embolism in patients undergoing a laterally extended parametrectomy (LEP) procedure.
Tarnai, L., Ungár, L., Pálfalvi, L., & Nagy, Z. (2011) · PubMed · Cited by 2
Artificial Intelligence in Clinical Immunology
Chin, A. T., & Rider, N. L. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_83 · Cited by 2
Tábuas‐Pereira, M., Guerreiro, R. J., Kun‐Rodrigues, C., Almeida, M. R., Brás, J., & Santana, I. J. (2022) · Neurogenetics · doi:10.1007/s10048-022-00699-0 · Cited by 2
Sha, Z., Chen, Y., & Hu, T. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad010 · Cited by 2
A Collection of Benchmark Data Sets for Knowledge Graph-Based Similarity in the Biomedical Domain
Cardoso, C., Sousa, R. T., Köhler, S., & Pesquita, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62327-2_9 · Cited by 2
Self-citationThe Aliment to Bodily Condition knowledgebase (ABCkb): a database connecting plants and human health
Trautman, A., Linchangco, R., Walstead, R., Jay, J. J., & Brouwer, C. (2021) · BMC Research Notes · doi:10.1186/s13104-021-05835-x · Cited by 2
Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice
Wong, D., Olivera, M., Yu, J., Szabó, A., Moghul, I., Balaskas, K., Luben, R., Khawaja, A. P., Pontikos, N., & Keane, P. A. (2021) · Modeling and Artificial Intelligence in Ophthalmology · doi:10.35119/maio.v3i1.115 · Cited by 2
VarSight: Prioritizing Clinically Reported Variants with Binary Classification AlgorithmsPreprint
Holt, J., Wilk, B. M., Birch, C. L., Brown, D. M., Gajapathy, M., Moss, A. C., Sosonkina, N., Wilk, M. A., Anderson, J. A., Harris, J. M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/532440 · Cited by 2
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
