AberOWL | Ontology repository and reasoning-as-a-service | Hoehndorf, R., Slater, L., Schofield, P. N., & Gkoutos, G. V. (2015). Aber-OWL: a framework for ontology-based data access in biology. BMC Bioinformatics, 16(1). https://doi.org/10.1186/s12859-015-0456-9 |
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PhenomeNET | Cross-species phenotype ontology and similarity computation | Hoehndorf, R., Schofield, P. N., & Gkoutos, G. V. (2011). PhenomeNET: a whole-phenome approach to disease gene discovery. Nucleic Acids Research, 39(18), e119–e119. https://doi.org/10.1093/nar/gkr538 |
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PathoPhenoDB | Database of pathogen-to-phenotype associations | Kafkas, Ş., Abdelhakim, M., Hashish, Y., Kulmanov, M., Abdellatif, M., Schofield, P. N., & Hoehndorf, R. (2019). PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0090-x |
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ICDPheno | Phenotypes associated with diseases in the ICD | Kafkas, Ş., Althubaiti, S., Gkoutos, G. V., Hoehndorf, R., & Schofield, P. N. (2021). Linking common human diseases to their phenotypes; development of a resource for human phenomics. Journal of Biomedical Semantics, 12(1). https://doi.org/10.1186/s13326-021-00249-x |
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DDIEM | Drug Database for Inborn Error of Metabolism | Abdelhakim, M., McMurray, E., Syed, A. R., Kafkas, S., Kamau, A. A., Schofield, P. N., & Hoehndorf, R. (2020). DDIEM: drug database for inborn errors of metabolism. Orphanet Journal of Rare Diseases, 15(1). https://doi.org/10.1186/s13023-020-01428-2 |
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Phenotype Reactor | Database and website of phenotype associations | |
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EDAM-annotated ontologies | A manually curated list of all ontologies in AberOWL annotated by topic, species and NCBI taxonomy | Rodríguez-García MÁ, Slater L, Boudellioua I, Schofield P, Gkoutos G, Hoehndorf R (2016)Updates to the AberOWL ontology repository (ICBO BioCreative 2016) |
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PhenomeNet Similarity Matrix (Human) | A matrix of PhenomeNet similarity scores between genes and OMIM IDs using human phenotypes only | Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500
Hoehndorf R, Schofield PN, Gkoutos GV (2011) PhenomeNET: a whole-phenome approach to disease gene discovery. Nuclein Acids Research, https://doi.org/10.1093/nar/gkr538 |
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PhenomeNet Similarity Matrix (Mouse) | A matrix of PhenomeNet similarity scores between genes and OMIM IDs using mouse phenotypes only | Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500
Hoehndorf R, Schofield PN, Gkoutos GV (2011) PhenomeNET: a whole-phenome approach to disease gene discovery. Nuclein Acids Research, https://doi.org/10.1093/nar/gkr538 |
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Raw Exomes | A set of synthetic exomes (VCF File Format) generated using ClinVar variants | Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500 |
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Raw Exomes (MAF) | A set of synthetic exomes (VCF File Format) generated using ClinVar variants, filtered by MAF < 1% | Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500 |
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Raw Genomes | A set of synthetic genomes (VCF File Format) generated using ClinVar variants | Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500 |
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Raw Genomes (MAF) | A set of synthetic genomes (VCF File Format) generated using ClinVar variants, filtered by MAF < 1% | Boudellioua I, Razali RBM, Kulmanov M, Hashush Y, Bajic V, Goncalves-Serra E, Schoenmakers N, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology, https://doi.org/10.1371/journal.pcbi.1005500 |
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RDF Knowledge Graph of proteins, drugs, diseases, functions and phenotypes | An RDF dataset containing data on the following semantic types, gene, drug, protein function, disease | Alshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, Hoehndorf R (2017) Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics btx275. doi:10.1093/bioinformatics/btx275 |
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Knowledge Graph embeddings | A knowledge graph embeddings containing of the RDF dataset mentioned above | Alshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, Hoehndorf R (2017) Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics btx275. doi:10.1093/bioinformatics/btx275 |
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Protein-protein interaction network embeddings for uniprot proteins | Network embeddings generated with knowledge graph representation learning method | Kulmanov M, Khan MA, Hoehndorf R (2017) DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics, btx624, https://doi.org/10.1093/bioinformatics/btx624 |
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Functions of CAFA3 targets | Protein function predictions for CAFA3 targets by using DeepGO | Kulmanov M, Khan MA, Hoehndorf R (2017) DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier. Bioinformatics, btx624, https://doi.org/10.1093/bioinformatics/btx624 |
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SIDER2DO | Mapping between indications in SIDER and Disease Ontology | Hoehndorf R, Schofield PN, Gkoutos GV (2015) Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Scientific Reports, doi: 10.1038/srep10888 |
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Disease Similarity Matrix | A disease-disease similarity matrix based on phenotype similarity, using Human Disease Ontology to represent diseases | Hoehndorf R, Schofield PN, Gkoutos GV (2015) Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Scientific Reports, doi: 10.1038/srep10888 |
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Disease-Disease drug similarity | A file containing pairs of diseases (using Human Disease Ontology) treated with the same drugs | Hoehndorf R, Schofield PN, Gkoutos GV (2015) Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Scientific Reports, doi: 10.1038/srep10888 |
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