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Recent Submissions

  • Multi-faceted semantic clustering with text-derived phenotypes.

    Slater, Luke T; Williams, John A; Karwath, Andreas; Fanning, Hilary; Ball, Simon; Schofield, Paul N; Hoehndorf, Robert; Gkoutos, Georgios V (Computers in biology and medicine, Elsevier BV, 2021-09-27) [Article]
    Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.
  • Linking common human diseases to their phenotypes; development of a resource for human phenomics

    Kafkas, Senay; Althubaiti, Sara; Gkoutos, Georgios V.; Hoehndorf, Robert; Schofield, Paul N. (Journal of Biomedical Semantics, Springer Science and Business Media LLC, 2021-08-23) [Article]
    Abstract Background In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. Methods We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10–phenotype associations from existing resources containing known relationships. Results We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease–phenotype associations, measuring their similarity to disease–phenotype associations found in public databases, and assessing how well they could be used to recover gene–disease associations using phenotype similarity. Conclusion We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at 10.5281/zenodo.4726713.
  • The COVID-19 epidemiology and monitoring ontology

    Queralt-Rosinach, Núria; Schofield, Paul N.; Hoehndorf, Robert; Weiland, Claus; Schultes, Erik Anthony; Bernabé, César Henrique; Roos, Marco (Center for Open Science, 2021-08-11) [Preprint]
    The novel COVID-19 infectious disease emerged and spread, causing high mortality and morbidity rates worldwide. In the OBO Foundry, there are more than one hundred ontologies to share and analyse large-scale datasets for biological and biomedical sciences. However, this pandemic revealed that we lack tools for an efficient and timely exchange of this epidemiological data which is necessary to assess the impact of disease outbreaks, the efficacy of mitigating interventions and to provide a rapid response. In this study we present our findings and contributions for the bio-ontologies community.
  • Evaluating Semantic Similarity Methods for Comparison of Text-derived Phenotype Profiles

    Slater, Luke T; Russell, Sophie; Makepeace, Silver; Carberry, Alexander; Karwath, Andreas; Williams, John A; Fanning, Hilary; Ball, Simon; Hoehndorf, Robert; Gkoutos, Georgios (Cold Spring Harbor Laboratory, 2021-08-09) [Preprint]
    Semantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance 'patient-like me' analyses, automated coding, differential diagnosis, and outcome prediction, by leveraging the wealth of background knowledge provided by biomedical ontologies. While a large body of work exists exploring the use of semantic similarity for multiple tasks, including protein interaction prediction, and rare disease differential diagnosis, there is less work exploring comparison of patient phenotype profiles for clinical tasks. Moreover, there are no experimental explorations of optimal parameters or methods in the area. In this work, we develop a reproducible platform for benchmarking experimental conditions for patient phentoype similarity. Using the platform, we evaluate the task of ranking shared primary diagnosis from uncurated phenotype profiles derived from text narrative associated with admissions in MIMIC-III. In doing this, we identify and interpret the performance of a large number of semantic similarity measures for this task, and provide a basis for further research on related tasks in the area.
  • DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

    Hinnerichs, Tilman; Hoehndorf, Robert (Bioinformatics, Oxford University Press (OUP), 2021-07-28) [Article]
    Motivation In silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks. Results We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.
  • DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web

    Kulmanov, Maxat; Zhapa-Camacho, Fernando; Hoehndorf, Robert (Nucleic Acids Research, Oxford University Press (OUP), 2021-05-21) [Article]
    Abstract Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic Web technologies. DeepGOWeb provides accurate and fast predictions and ensures that predicted functions are consistent with the Gene Ontology; it can provide predictions for any protein and any function in Gene Ontology. DeepGOWeb is freely available at https://deepgo.cbrc.kaust.edu.sa/.
  • Towards Similarity-based Differential Diagnostics For Common Diseases

    Slater, Luke T; Karwath, Andreas; Williams, John A.; Russell, Sophie; Makepeace, Silver; Carberry, Alexander; Hoehndorf, Robert; Gkoutos, Georgios (Computers in Biology and Medicine, Elsevier BV, 2021-04-01) [Article]
    Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.
  • DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration

    Althubaiti, Sara; Kulmanov, Maxat; Liu, Yang; Gkoutos, Georgios; Schofield, Paul N.; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2021-03-03) [Preprint]
    Combining multiple types of genomic, transcriptional, proteomic, and epigenetic datasets has the potential to reveal biological mechanisms across multiple scales, and may lead to more accurate models for clinical decision support. Developing efficient models that can derive clinical outcomes from high-dimensional data remains problematical; challenges include the integration of multiple types of omics data, inclusion of biological background knowledge, and developing machine learning models that are able to deal with this high dimensionality while having only few samples from which to derive a model. We developed DeepMOCCA, a framework for multi-omics cancer analysis. We combine different types of omics data using biological relations between genes, transcripts, and proteins, combine the multi-omics data with background knowledge in the form of protein-protein interaction networks, and use graph convolution neural networks to exploit this combination of multi-omics data and background knowledge. DeepMOCCA predicts survival time for individual patient samples for 33 cancer types and outperforms most existing survival prediction methods. Moreover, DeepMOCCA includes a graph attention mechanism which prioritizes driver genes and prognostic markers in a patient-specific manner; the attention mechanism can be used to identify drivers and prognostic markers within cohorts and individual patients.
  • DeepViral: prediction of novel virus-host interactions from protein sequences and infectious disease phenotypes.

    Liu-Wei, Wang; Kafkas, Senay; Chen, Jun; Dimonaco, Nicholas J; Tegner, Jesper; Hoehndorf, Robert (Bioinformatics, Oxford University Press (OUP), 2021-03-03) [Article]
    MotivationInfectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.ResultsWe developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction.AvailabilityCode and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824.
  • DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learning

    Althagafi, Azza Th.; Alsubaie, Lamia; Kathiresan, Nagarajan; Mineta, Katsuhiko; Aloraini, Taghrid; Almutairi, Fuad; Alfadhel, Majid; Gojobori, Takashi; Alfares, Ahmed; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2021-01-28) [Preprint]
    Motivation: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity, and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants, as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. Results: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic information with information about gene functions. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual celltypes, and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. Availability: https://github.com/bio-ontology-research-group/DeepSVP Contact: robert.hoehndorf@kaust.edu.sa
  • reality/mimpred:

    Slater, Luke T; Russell, Sophie; Makepeace, Silver; Carberry, Alexander; Karwath, Andreas; Williams, John A; Fanning, Hilary; Ball, Simon; Hoehndorf, Robert; Gkoutos, Georgios (Github, 2020-12-01) [Software]
  • NuriaQueralt/covid19-epidemiology-ontology: Epidemiology and monitoring ontology for COVID-19

    Queralt-Rosinach, Núria; Schofield, Paul N.; Hoehndorf, Robert; Weiland, Claus; Schultes, Erik Anthony; Bernabé, César Henrique; Roos, Marco (Github, 2020-11-09) [Software]
    Epidemiology and monitoring ontology for COVID-19
  • Predicting Candidate Genes From Phenotypes, Functions, And Anatomical Site Of Expression.

    Chen, Jun; Althagafi, Azza Th.; Hoehndorf, Robert (Bioinformatics, Oxford University Press (OUP), 2020-10-14) [Article]
    MOTIVATION:Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models. RESULTS:We developed a novel graph-based machine learning method for biomedical ontologies which is able to exploit axioms in ontologies and other graph-structured data. Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression. We then develop a machine learning model to predict gene-disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state of the art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes which are associated with phenotypes, functions, or site of expression. AVAILABILITY:Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.
  • Semantic similarity and machine learning with ontologies.

    Kulmanov, Maxat; Smaili, Fatima Z.; Gao, Xin; Hoehndorf, Robert (Briefings in bioinformatics, Oxford University Press (OUP), 2020-10-13) [Article]
    Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.
  • EMC10 Homozygous Variant Identified in a Family with Global Developmental Delay, Mild Intellectual Disability, and Speech Delay.

    Umair, Muhammad; Ballow, Mariam; Asiri, Abdulaziz; Alyafee, Yusra; Al Tuwaijri, Abeer; Alhamoudi, Kheloud M; Aloraini, Taghrid; Abdelhakim, Marwa; Althagafi, Azza Th.; Kafkas, Senay; Alsubaie, Lamia; Alrifai, Muhammad Talal; Hoehndorf, Robert; Alfares, Ahmed; Alfadhel, Majid (Clinical genetics, Wiley, 2020-09-15) [Article]
    In recent years, several genes have been implicated in the variable disease presentation of global developmental delay (GDD) and intellectual disability (ID). The endoplasmic reticulum membrane protein complex (EMC) family is known to be involved in GDD and ID. Homozygous variants of EMC1 are associated with GDD, scoliosis, and cerebellar atrophy, indicating the relevance of this pathway for neurogenetic disorders. EMC10 is a bone marrow-derived angiogenic growth factor that plays an important role in infarct vascularization and promoting tissue repair. However, this gene has not been previously associated with human disease. Herein, we describe a Saudi family with two individuals segregating a recessive neurodevelopmental disorder. Both of the affected individuals showed mild ID, speech delay, and GDD. Whole-exome sequencing (WES) and Sanger sequencing were performed to identify candidate genes. Further, to elucidate the functional effects of the variant, quantitative real-time PCR (RT-qPCR)-based expression analysis was performed. WES revealed a homozygous splice acceptor site variant (c.679-1G > A) in EMC10 (chromosome 19q13.33) that segregated perfectly within the family. RT-qPCR showed a substantial decrease in the relative EMC10 gene expression in the patients, indicating the pathogenicity of the identified variant. For the first time in the literature, the EMC10 gene variant was associated with mild ID, speech delay, and GDD. Thus, this gene plays a key role in developmental milestones, with the potential to cause neurodevelopmental disorders in humans. This article is protected by copyright. All rights reserved.
  • Komenti: A semantic text mining framework

    Slater, Luke T; Bradlow, William; Hoehndorf, Robert; Motti, Dino FA; Ball, Simon; Gkoutos, Georgios (Cold Spring Harbor Laboratory, 2020-08-05) [Preprint]
    Komenti is a reasoner-enabled semantic query and information extraction tool. It is the only text mining tool that enables querying inferred knowledge from biomedical ontologies. It also contains multiple novel components for vocabulary construction and context disambiguation, which can improve the power of text mining and ontology-based analysis tasks, with a view towards making full use of the semantic provision of biomedical ontologies in the text extraction and characterisation space. Here, we describe Komenti, its features, and a use case wherein we automate a clinical audit process, classifying the medications of patients with hypertrophic cardiomyopathy from text records, revealing a high precision, and a subcohort of candidate patients who have atrial fibrillation but were not anti-coagulated, and are therefore at a higher risk of stroke.
  • What is the right sequencing approach? Solo VS extended family analysis in consanguineous populations.

    Alfares, Ahmed; Alsubaie, Lamia; Aloraini, Taghrid; Alaskar, Aljoharah; Althagafi, Azza Th.; Alahmad, Ahmed; Rashid, Mamoon; Alswaid, Abdulrahman; Alothaim, Ali; Eyaid, Wafaa; Ababneh, Faroug; Albalwi, Mohammed; Alotaibi, Raniah; Almutairi, Mashael; Altharawi, Nouf; Alsamer, Alhanouf; Abdelhakim, Marwa; Kafkas, Senay; Mineta, Katsuhiko; Cheung, Nicole; Abdallah, Abdallah; Büchmann-Møller, Stine; Fukasawa, Yoshinori; Zhao, Xiang; Rajan, Issaac; Hoehndorf, Robert; Al Mutairi, Fuad; Gojobori, Takashi; Alfadhel, Majid (BMC medical genomics, Springer Nature, 2020-07-17) [Article]
    BACKGROUND:Testing strategies is crucial for genetics clinics and testing laboratories. In this study, we tried to compare the hit rate between solo and trio and trio plus testing and between trio and sibship testing. Finally, we studied the impact of extended family analysis, mainly in complex and unsolved cases. METHODS:Three cohorts were used for this analysis: one cohort to assess the hit rate between solo, trio and trio plus testing, another cohort to examine the impact of the testing strategy of sibship genome vs trio-based analysis, and a third cohort to test the impact of an extended family analysis of up to eight family members to lower the number of candidate variants. RESULTS:The hit rates in solo, trio and trio plus testing were 39, 40, and 41%, respectively. The total number of candidate variants in the sibship testing strategy was 117 variants compared to 59 variants in the trio-based analysis. We noticed that the average number of coding candidate variants in trio-based analysis was 1192 variants and 26,454 noncoding variants, and this number was lowered by 50-75% after adding additional family members, with up to two coding and 66 noncoding homozygous variants only, in families with eight family members. CONCLUSION:There was no difference in the hit rate between solo and extended family members. Trio-based analysis was a better approach than sibship testing, even in a consanguineous population. Finally, each additional family member helped to narrow down the number of variants by 50-75%. Our findings could help clinicians, researchers and testing laboratories select the most cost-effective and appropriate sequencing approach for their patients. Furthermore, using extended family analysis is a very useful tool for complex cases with novel genes.
  • Modeling quantitative traits for COVID-19 case reports

    Queralt-Rosinach, Núria; Bello, Susan; Hoehndorf, Robert; Weiland, Claus; Rocca-Serra, Philippe; Schofield, Paul N. (Cold Spring Harbor Laboratory, 2020-06-21) [Preprint]
    Medical practitioners record the condition status of a patient through qualitative and quantitative observations. The measurement of vital signs and molecular parameters in the clinics gives a complementary description of abnormal phenotypes associated with the progression of a disease. The Clinical Measurement Ontology (CMO) is used to standardize annotations of these measurable traits. However, researchers have no way to describe how these quantitative traits relate to phenotype concepts in a machine-readable manner. Using the WHO clinical case report form standard for the COVID-19 pandemic, we modeled quantitative traits and developed OWL axioms to formally relate clinical measurement terms with anatomical, biomolecular entities and phenotypes annotated with the Uber-anatomy ontology (Uberon), Chemical Entities of Biological Interest (ChEBI) and the Phenotype and Trait Ontology (PATO) biomedical ontologies. The formal description of these relations allows interoperability between clinical and biological descriptions, and facilitates automated reasoning for analysis of patterns over quantitative and qualitative biomedical observations.
  • bio-ontology-research-group/DeepSVP: Prioritizing Copy Number Variants (CNV) using Phenotype and Gene Functional Similarity

    Althagafi, Azza Th.; Alsubaie, Lamia; Kathiresan, Nagarajan; Mineta, Katsuhiko; Aloraini, Taghrid; Almutairi, Fuad; Alfadhel, Majid; Gojobori, Takashi; Alfares, Ahmed; Hoehndorf, Robert (Github, 2020-06-08) [Software]
    Prioritizing Copy Number Variants (CNV) using Phenotype and Gene Functional Similarity
  • Machine learning with biomedical ontologies

    Kulmanov, Maxat; Smaili, Fatima Z.; Gao, Xin; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2020-05-08) [Preprint]
    Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge, and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in biomedical ontologies, and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.Key pointsOntologies provide background knowledge that can be exploited in machine learning models.Ontology embeddings are structure-preserving maps from ontologies into vector spaces and provide an important method for utilizing ontologies in machine learning. Embeddings can preserve different structures in ontologies, including their graph structures, syntactic regularities, or their model-theoretic semantics.Axioms in ontologies, in particular those involving negation, can be used as constraints in optimization and machine learning to reduce the search space.

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