Applied Ontology

Datamining with Ontologies

Year: 2016 Venue: Data Mining Techniques for the Life Sciences Authors: Robert Hoehndorf, Georgios V. Gkoutos, Paul N. Schofield DOI: 10.1007/978-1-4939-3572-7_19 Abstract The use of ontologies has increased rapidly over the past decade and they now provide a key component of most major databases in biology and biomedicine. Consequently, datamining over these databases benefits from considering the specific structure and content of ontologies, and several methods have been developed to use ontologies in datamining applications. Here, we discuss the principles of ontology structure, and

JOWO 2020: The Joint Ontology Workshops : Proceedings of the Joint Ontology Workshops co-located with the Bolzano Summer of Knowledge (BOSK 2020)

Applied Ontology Ontology engineering

Year: 2020 Venue: CEUR-WS Topics Applied Ontology · Ontology engineering

Argumentation to Represent and Reason over Biological Systems

Applied Ontology

Year: 2012 Venue: Proceedings of the 3rd International Conference on Information Technology in Bio- and Medical Informatics (ITBAM 2012) Authors: Adam Wyner, Luke Riley, Robert Hoehndorf, Samuel Croset Topics Applied Ontology

Exploring Gene Ontology Annotations with OWL

Applied Ontology protein function

Year: 2011 Venue: Proceedings of the 13th Bio-Ontology Meeting Authors: Simon Jupp, Robert Stevens, Robert Hoehndorf Abstract Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other activities. Tools, such as AmiGO, allow exploration of genes based on their GO annotations. This human driven exploration and querying of GO is obviously useful, but by taking advantage of the ontological representation we can use these annotations to create a rich polyhierarchy of gene products for enhanced querying

Investigation of the fundamental strategy for interoperability of description of biological measurements

Applied Ontology Ontology engineering

Year: 2011 Venue: Proceedings of the Second International Conference on Biomedical Ontology Authors: Hiroshi Masuya, Georgios V. Gkoutos, Nobuhiko Tanaka, Kazunori Waki, Yoshihiro Okuda, Tatsuya Kushida, Norio Kobayashi, Koji Doi, Kouji Kozaki, Robert Hoehndorf, Shigeharu Wakana, Tetsuro Toyoda, Riichiro Mizoguchi Abstract Aiming the facilitation of the advanced integration of measurement data across various biological experiments, we have investigated the fundamental methodology to expand the Phenotypic Quality Ontology (PATO) commonly used for descriptions of biological phenotypes with the

Higgs bosons, mars missions, and unicorn delusions: How to deal with terms of dubious reference in scientific ontologies

Applied Ontology

Year: 2011 Venue: Proceedings of the Second International Conference on Biomedical Ontology Authors: Stefan Schulz, Mathias Brochhausen, Robert Hoehndorf Abstract Realist ontologies claim to represent what exists. Scientific discourse, however, often contains non-referring terms when describing hypotheses, plans, or ideas. We present a framework in which a realist ontology is embedded in an description logics theory, which is indifferent regarding the existence of class members, and which may include representational units for representing various kinds of non-referring terms. Using a taxonomy

Fully Geometric Multi-hop Reasoning on Knowledge Graphs with Transitive Relations

Neuro-Symbolic AI Ontology engineering Applied Ontology

Year: 2026 Venue: The Semantic Web -- ESWC 2026 Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1007/978-3-032-25156-5_14 Abstract Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space. Current geometric embedding methods show to be useful on this task by mapping entities to geometric regions and logical operations to latent transformations. While a geometric embedding can provide a direct interpretability framework for query answering, current methods have only leveraged the geometric construction of entities, failing to

Using SPARQL to Unify Queries over Data, Ontologies, and Machine Learning Models in the PhenomeBrowser Knowledgebase

Ontology engineering Applied Ontology

Year: 2022 Venue: Proceedings of the 13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022 Authors: Ali Syed, Senay Kafkas, Maxat Kulmanov, Robert Hoehndorf Topics Ontology engineering · Applied Ontology Acknowledged projects ccf-microbial-cell-factories crg-complex-variant-prioritization crg-ibnsina-qi

Ontology based mining of pathogen-disease associations from literature

Biomedical Informatics Applied Ontology

Year: 2018 Venue: Bio-Ontologies COSI Authors: Senay Kafkas, Robert Hoehndorf Topics Biomedical informatics · Applied Ontology

To MIREOT or not to MIREOT? A case study of the impact of using MIREOT in the Experimental Factor Ontology (EFO)

Ontology engineering Applied Ontology

Year: 2016 Venue: International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016) Authors: Luke Slater, Georgios V. Gkoutos, Paul N Schofield, Robert Hoehndorf Abstract MIREOT is a mechanism for the selective re-use of individual ontology classes in other ontologies. Designed to minimise effort and to support orthogonality, it is now in widespread use. The consequences for ontology integrity and automated reasoning of using the MIREOT mechanism have so far not been fully assessed. In this paper, we perform an analysis of the Experimental Factor Ontology (EFO), an

Large-Scale Reasoning over Functions in Biomedical Ontologies

Applied Ontology Ontology engineering

Year: 2016 Venue: Formal Ontology in Information Systems Authors: Robert Hoehndorf, Liam Mencel, Georgios V. Gkoutos, Paul N. Schofield Abstract A large number of biomedical resources have been developed to represent the functions of biological entities, and these resources are widely used for data integration and analysis. Expressing functions in biomedical ontologies currently uses formal representation patterns that renders basic reasoning tasks to fall in complexity classes beyond polynomial time, thereby limiting the potential of using knowledge-based methods for data integration

Updating the CEMO ontology for future epidemiological challenges

Applied Ontology

Year: 2023 Venue: 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023), Basel, Switzerland, February 13-16, 2023 Authors: N\'uria Queralt-Rosinach, Paul N. Schofield, Marco Roos, Robert Hoehndorf Abstract The COVID-19 epidemiology and monitoring ontology (CEMO) is an OWL ontology built during the COVID-19 pandemic for better exchange, integration and reuse of epidemiological information. Here, we present an update of the development of the ontology and future directions in order to make it usable under different scenarios and

Exploring the Use of Ontology Components for Distantly-Supervised Disease and Phenotype Named Entity Recognition

Applied Ontology

Year: 2023 Venue: Proceedings of the International Conference on Biomedical Ontologies 2023 together with the Workshop on Ontologies for Infectious and Immune-Mediated Disease Data Science (OIIDDS 2023) and the FAIR Ontology Harmonization and TRUST Data Interoperability Workshop (FOHTI 2023), Bras\', Brazil, August 28 - September 1, 2023 Authors: Sumyyah Toonsi, Senay Kafkas, Robert Hoehndorf Abstract The lack of curated corpora is one of the major obstacles for Named Entity Recognition (NER). With the advancements in deep learning and development of robust language models, distant supervision

Semantic Systems Biology: Formal Knowledge Representation in Systems Biology for Model Construction, Retrieval, Validation and Discovery

Applied Ontology Drug mechanisms

Year: 2013 Venue: Systems Biology Authors: Michel Dumontier, Leonid L Chepelev, Robert Hoehndorf Abstract With the publication of the human genome, scientists worldwide opened champagne and let out a collective cheer for progress in biology. After all, the untold number of interactions of tens of thousands of genes, a greater number of their products and product derivatives, and tens of thousands of chemicals came much closer to complete characterization. Paradoxically however, while individual efforts produced important biological results, an integrated view of biology from systems

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

Biomedical Informatics Applied Ontology

Year: 2013 Venue: PLoS ONE Authors: Dietrich Rebholz-Schuhmann, Jee-Hyub Kim, Ying Yan, Abhishek Dixit, Caroline Friteyre, Robert Hoehndorf, Rolf Backofen, Ian Lewin Abstract Motivation: Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical "term space" (the "Lexeome"), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to

Representing physiological processes and their participants with PhysioMaps

Applied Ontology

Year: 2013 Venue: Journal of Biomedical Semantics Authors: Daniel Cook, Maxwell Neal, Robert Hoehndorf, Georgios Gkoutos, John Gennari DOI: 10.1186/2041-1480-4-S1-S2 Abstract BACKGROUND:As the number and size of biological knowledge resources for physiology grows, researchers need improved tools for searching and integrating knowledge and physiological models. Unfortunately, current resources--databases, simulation models, and knowledge bases, for example--are only occasionally and idiosyncratically explicit about the semantics of the biological entities and processes that they describe

Chapter Four - The Neurobehavior Ontology: An Ontology for Annotation and Integration of Behavior and Behavioral Phenotypes

Applied Ontology Phenotype informatics Ontology engineering

Year: 2012 Venue: Bioinformatics of Behavior: Part 1 Authors: Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf DOI: 10.1016/B978-0-12-388408-4.00004-6 Abstract Abstract In recent years, considerable advances have been made toward our understanding of the genetic architecture of behavior and the physical, mental, and environmental influences that underpin behavioral processes. The provision of a method for recording behavior-related phenomena is necessary to enable integrative and comparative analyses of data and knowledge about behavior. The neurobehavior ontology facilitates the

The Units Ontology: a tool for integrating units of measurement in science

Applied Ontology Ontology engineering

Year: 2012 Venue: Database Authors: Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract Units are basic scientific tools that render meaning to numerical data. Their standardization and formalization caters for the report, exchange, process, reproducibility and integration of quantitative measurements. Ontologies are means that facilitate the integration of data and knowledge allowing interoperability and semantic information processing between diverse biomedical resources and domains. Here, we present the Units Ontology (UO), an ontology currently being used in many scientific

Evaluation of research in biomedical ontologies

Applied Ontology Biomedical Informatics

Year: 2012 Venue: Briefings in Bioinformatics Authors: Robert Hoehndorf, Michel Dumontier, Georgios V. Gkoutos Abstract Ontologies are now pervasive in biomedicine, where they serve as a means to standardize terminology, to enable access to domain knowledge, to verify data consistency and to facilitate integrative analyses over heterogeneous biomedical data. For this purpose, research on biomedical ontologies applies theories and methods from diverse disciplines such as information management, knowledge representation, cognitive science, linguistics and philosophy. Depending on the desired

Ontology-based cross-species integration and analysis of Saccharomyces cerevisiae phenotypes

Applied Ontology Phenotype informatics

Year: 2011 Venue: Proceedings of the 3rd Workshop for Ontologies in Biomedicine and Life sciences (OBML) Authors: Georgios V. Gkoutos, Robert Hoehndorf Abstract Ontologies are widely used in the biomedical community for annotation and integration of databases. Formal definitions can relate classes from different ontologies and thereby integrate data across different levels of granularity, domains and species. We have applied this methodology to the Ascomycete Phenotype Ontology (APO), enabling the reuse of various orthogonal ontologies and we have converted the phenotype associated data found

Towards Improving Phenotype Representation in OWL

Applied Ontology Phenotype informatics

Year: 2011 Venue: Proceedings of the 3rd Workshop for Ontologies in Biomedicine and Life sciences (OBML) Authors: Frank Loebe, Frank Stumpf, Robert Hoehndorf, Heinrich Herre Abstract Phenotype ontologies are used in species-specific databases for the annotation of mutagenesis experiments and to characterize hu- man diseases. The Entity-Quality (EQ) formalism is a means to describe complex phenotypes based on one or more affected en- tities and a quality. EQ-based definitions have been developed for many phenotype ontologies, including the Human and Mammalian Phenotype ontologies. We analyze

Semantic integration of physiology phenotypes with an application to the Cellular Phenotype Ontology

Applied Ontology Phenotype informatics Ontology engineering

Year: 2012 Venue: Bioinformatics Authors: Robert Hoehndorf, Midori A. Harris, Heinrich Herre, Gabriella Rustici, Georgios V. Gkoutos DOI: 10.1093/bioinformatics/bts250 Abstract Motivation: The systematic observation of phenotypes has become a crucial tool of functional genomics, and several large international projects are currently underway to identify and characterize the phenotypes that are associated with genotypes in several species. To integrate phenotype descriptions within and across species, phenotype ontologies have been developed. Applying ontologies to unify phenotype descriptions

Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL

Applied Ontology protein function Ontology engineering

Venue: Journal of Biomedical Semantics Authors: Simon Jupp, Robert Stevens, Robert Hoehndorf Abstract MOTIVATION:Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products

Linking PharmGKB to phenotype studies and animal models of disease for drug repurposing

Drug mechanisms Rare disease Applied Ontology

Venue: Pacific Symposium on Biocomputing (PSB) Authors: Robert Hoehndorf, Anika Oellrich, Dietrich Rebholz-Schuhmann, Paul N. Schofield, Georgios V. Gkoutos Abstract The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype--disease association, combine them with drug--gene associations available from the PharmGKB database, and

OBML - Ontologies in Biomedicine and Life Sciences

Applied Ontology Biomedical Informatics

Venue: Journal of Biomedical Semantics Authors: Heinrich Herre, Robert Hoehndorf, Janet Kelso, Frank Loebe, Stefan Schulz DOI: 10.1186/2041-1480-2-S4-I1 Abstract The OBML 2010 workshop, held at the University of Mannheim on September 9-10, 2010, is the 2nd in a series of meetings organized by the Working Group "Ontologies in Biomedicine and Life Sciences" of the German Society of Computer Science (GI) and the German Society of Medical Informatics, Biometry and Epidemiology (GMDS). Integrating, processing and applying the rapidly expanding information generated in the life sciences -- from

New approaches to the representation and analysis of phenotype knowledge in human diseases and their animal models

Applied Ontology Phenotype informatics Rare disease

Venue: Briefings in Functional Genomics Authors: Paul N. Schofield, John P. Sundberg, Robert Hoehndorf, Georgios V. Gkoutos Abstract The systematic investigation of the phenotypes associated with genotypes in model organisms holds the promise of revealing genotype–phenotype relations directly and without additional, intermediate inferences. Large-scale projects are now underway to catalog the complete phenome of a species, notably the mouse. With the increasing amount of phenotype information becoming available, a major challenge that biology faces today is the systematic analysis of this

Ontology design patterns to disambiguate relations between genes and gene products in GENIA

Applied Ontology Ontology engineering Biomedical Informatics

Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Axel-Cyrille Ngonga Ngomo, Sampo Pyysalo, Tomoko Ohta, Anika Oellrich, Dietrich Rebholz-Schuhmann DOI: 10.1186/2041-1480-2-S5-S1 Abstract MOTIVATION:Annotated reference corpora play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies is challenging due to the inherent ambiguity of natural language. The provision of formal definitions and axioms for semantic annotations offers the means for ensuring consistency as well as

PIDO: The Primary Immunodeficiency Disease Ontology

Applied Ontology Rare disease

Venue: Bioinformatics Authors: Nico Adams, Robert Hoehndorf, Georgios V. Gkoutos, Gesine Hansen, Christian Hennig Abstract Motivation: Primary Immunodeficiency Diseases (PIDs) are Mendelian conditions of high phenotypic complexity and low incidence. They usually manifest in toddlers and infants, although they can also occur much later in life. Information about PIDs is often widely scattered throughout the clinical as well as the research literature and hard to find for both generalists as well as experienced clinicians. Semantic Web technologies coupled to clinical information systems can go

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning

Ontology engineering Applied Ontology

Venue: PLOS ONE Authors: Robert Hoehndorf, Michel Dumontier, Anika Oellrich, Dietrich Rebholz-Schuhmann, Paul N. Schofield, Georgios V. Gkoutos Abstract Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect

The RNA Ontology (RNAO): An Ontology for Integrating RNA Sequence and Structure Data

Applied Ontology Biomedical Informatics

Venue: Applied Ontology Authors: Robert Hoehndorf, Colin Batchelor, Thomas Bittner, Michel Dumontier, Karen Eilbeck, Rob Knight, Chris J. Mungall, Jane S. Richardson, Jesse Stombaugh, Eric Westhof, Craig L. Zirbel, Neocles B. Leontis Abstract Biomedical Ontologies integrate diverse biomedical data and enable intelligent data-mining and help translate basic research into useful clinical knowledge. We present the RNA Ontology (RNAO), an ontology for integrating diverse RNA data, including RNA sequences and sequence alignments, three-dimensional structures, and biochemical and functional data

A common layer of interoperability for biomedical ontologies based on OWL EL

Ontology engineering Applied Ontology

Venue: Bioinformatics Authors: Robert Hoehndorf, Michel Dumontier, Anika Oellrich, Sarala Wimalaratne, Dietrich Rebholz-Schuhmann, Paul N. Schofield, Georgios V. Gkoutos Abstract Motivation: Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their

Interoperability between phenotype and anatomy ontologies

Applied Ontology Ontology engineering Phenotype informatics

Venue: Bioinformatics Authors: Robert Hoehndorf, Anika Oellrich, Dietrich Rebholz-Schuhmann Abstract Motivation: Phenotypic information is important for the analysis of the molecular mechanisms underlying disease. A formal ontological representation of phenotypic information can help to identify, interpret and infer phenotypic traits based on experimental findings. The methods that are currently used to represent data and information about phenotypes fail to make the semantics of the phenotypic trait explicit and do not interoperate with ontologies of anatomy and other domains. Therefore

The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

Applied Ontology Ontology engineering

Year: 2014 Venue: Journal of Biomedical Semantics Authors: Michel Dumontier, Christopher Baker, Joachim Baran, Alison Callahan, Leonid Chepelev, Jose Cruz-Toledo, Nicholas Del Rio, Geraint Duck, Laura Furlong, Nichealla Keath, Dana Klassen, James McCusker, Nuria Queralt-Rosinach, Matthias Samwald, Natalia Villanueva-Rosales, Mark Wilkinson, Robert Hoehndorf Abstract The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real

Semantic units: organizing knowledge graphs into semantically meaningful units of representation

Applied Ontology

Year: 2024 Venue: Journal of Biomedical Semantics Authors: Lars Vogt, Tobias Kuhn, Robert Hoehndorf DOI: 10.1186/s13326-024-00310-5 Abstract Abstract Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution

Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies

Ontology engineering Applied Ontology

Year: 2020 Venue: BMC Medical Informatics and Decision Making Authors: Luke T. Slater, Georgios V. Gkoutos, Robert Hoehndorf DOI: 10.1186/s12911-020-01336-2 Abstract Abstract Background Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may

Ontology-based validation and identification of regulatory phenotypes

Applied Ontology Phenotype informatics

Year: 2018 Venue: Bioinformatics Authors: Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Motivation: Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the

The anatomy of phenotype ontologies: principles, properties and applications

Applied Ontology Phenotype informatics

Year: 2018 Venue: Briefings in Bioinformatics Authors: Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf DOI: https://doi.org/10.1093/bib/bbx035 Abstract The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological

Ontology based mining of pathogen--disease associations from literature

Biomedical Informatics Applied Ontology

Year: 2019 Venue: Journal of Biomedical Semantics Authors: Senay Kafkas, Robert Hoehndorf Topics Biomedical informatics · Applied Ontology Acknowledged projects ccf-microbial-cell-factories crg-bio2vec

Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

Neuro-Symbolic AI Applied Ontology

Year: 2018 Venue: Bioinformatics Authors: Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf Abstract Motivation: Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to

An ontology approach to comparative phenomics in plants

Applied Ontology Phenotype informatics

Year: 2015 Venue: Plant Methods Authors: Anika Oellrich, Ramona Walls, Ethalinda Cannon, Steven Cannon, Laurel Cooper, Jack Gardiner, Georgios Gkoutos, Lisa Harper, Mingze He, Robert Hoehndorf, Pankaj Jaiswal, Scott Kalberer, John Lloyd, David Meinke, Naama Menda, Laura Moore, Rex Nelson, Anuradha Pujar, Carolyn Lawrence, Eva Huala DOI: 10.1186/s13007-015-0053-y Abstract BACKGROUND:Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and

DELE: Deductive EL++ Embeddings for Knowledge Base Completion

Neuro-Symbolic AI Applied Ontology Ontology engineering

Year: 2026 Venue: Neurosymbolic Artificial Intelligence Authors: Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1177/29498732261420011 Abstract Ontology embeddings map classes, roles, and individuals in ontologies into R^n , and within R^n similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic EL^++ , several optimization-based embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations; they do not distinguish between statements that are

Evaluating the effect of annotation size on measures of semantic similarity

Semantic similarity Applied Ontology

Year: 2016 Venue: Proceedings of Bio-Ontologies SIG Authors: Maxat Kulmanov, Robert Hoehndorf Abstract Ontologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products. Here, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of

The flora phenotype ontology (FLOPO): tool for integrating morphological traits and phenotypes of vascular plants

Applied Ontology Phenotype informatics

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Mona Alshahrani, Georgios V. Gkoutos, George Gosline, Quentin Groom, Thomas Hamann, Jens Kattge, Sylvia Mota de Oliveira, Marco Schmidt, Soraya Sierra, Erik Smets, Rutger A. Vos, Claus Weiland DOI: 10.1186/s13326-016-0107-8 Abstract The systematic analysis of a large number of comparable plant trait data can support investigations into phylogenetics and ecological adaptation, with broad applications in evolutionary biology, agriculture, conservation, and the functioning of ecosystems. Floras, i.e., books collecting

The role of ontologies in biological and biomedical research: a functional perspective

Applied Ontology Biomedical Informatics

Year: 2015 Venue: Briefings in Bioinformatics Authors: Robert Hoehndorf, Paul N. Schofield, Georgios V. Gkoutos Abstract Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that

A Review of Current Standards and the Evolution of Histopathology Nomenclature for Laboratory Animals

Applied Ontology Ontology engineering

Year: 2018 Venue: ILAR Journal Authors: Charlotte M Keenan, Colin McKerlie, Georgios V Gkoutos, Jerrold M Ward, John P Sundberg, Mark F Cesta, Paul N Schofield, Robert Cardiff, Robert Hoehndorf, Susan A Elmore Abstract The need for international collaboration in rodent pathology has evolved since the 1970s and was initially driven by the new field of toxicologic pathology. First initiated by the World Health Organization’s International Agency for Research on Cancer for rodents, it has evolved to include pathology of the major species (rats, mice, guinea pigs, nonhuman primates, pigs, dogs

Notions of similarity for systems biology models

Semantic similarity Applied Ontology

Year: 2018 Venue: Briefings in Bioinformatics Authors: Ron Henkel, Robert Hoehndorf, Tim Kacprowski, Christian Knupfer, Wolfgang Liebermeister, Dagmar Waltemath Abstract Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of 'similarity' may greatly

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

Applied Ontology

Year: 2022 Venue: Journal of Biomedical Semantics Authors: Yongqun He, Hong Yu, Anthony Huffman, Asiyah Yu Lin, Darren A. Natale, John Beverley, Ling Zheng, Yehoshua Perl, Zhigang Wang, Yingtong Liu, Edison Ong, Yang Wang, Philip Huang, Long Tran, Jinyang Du, Zalan Shah, Easheta Shah, Roshan Desai, Hsin-hui Huang, Yujia Tian, Eric Merrell, William D. Duncan, Sivaram Arabandi, Lynn M. Schriml, Jie Zheng, Anna Maria Masci, Liwei Wang, Hongfang Liu, Fatima Zohra Smaili, Robert Hoehndorf, Zoe May Pendlington, Paola Roncaglia, Xianwei Ye, Jiangan Xie, Yi-Wei Tang, Xiaolin Yang, Suyuan Peng, Luxia

FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration

Applied Ontology Ontology engineering

Year: 2018 Venue: Science of Food Authors: Damion M. Dooley, Emma J. Griffiths, Gurinder S. Gosal, Pier L. Buttigieg, Robert Hoehndorf, Matthew C. Lange, Lynn M. Schriml, Fiona S. L. Brinkman, William W. L. Hsiao Topics Applied Ontology · Ontology engineering

DermO; an ontology for the description of dermatologic disease

Applied Ontology Phenotype informatics

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Hannah M. Fisher, Robert Hoehndorf, Bruno S. Bazelato, Soheil S. Dadras, Lloyd E. King, Georgios V. Gkoutos, John P. Sundberg, Paul N. Schofield DOI: 10.1186/s13326-016-0085-x Abstract There have been repeated initiatives to produce standard nosologies and terminologies for cutaneous disease, some dedicated to the domain and some part of bigger terminologies such as ICD-10. Recently, formally structured terminologies, ontologies, have been widely developed in many areas of biomedical research. Primarily, these address the aim of

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species

Applied Ontology Ontology engineering

Year: 2017 Venue: Scientific Reports Authors: Adil Salhi, Sonia Negrao, Magbubah Essack, Mitchell J. L. Morton, Salim Bougouffa, Rozaimi Mohamad Razali, Aleksandar Radovanovic, Benoit Marchand, Maxat Kulmanov, Robert Hoehndorf, Mark A. Tester, Vladimir B. Bajic Abstract Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

Applied Ontology Ontology engineering

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Jerven T. Bolleman, Christopher J. Mungall, Francesco Strozzi, Joachim Baran, Michel Dumontier, Raoul P. J. Bonnal, Robert Buels, Robert Hoehndorf, Takatomo Fujisawa, Toshiaki Katayama, Peter A. J. Cock DOI: 10.1186/s13326-016-0067-z Abstract Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as

Best behaviour? Ontologies and the formal description of animal behaviour

Applied Ontology Phenotype informatics

Year: 2015 Venue: Mammalian Genome Authors: Georgios V Gkoutos, Robert Hoehndorf, Loukia Tsaprouni, Paul N Schofield DOI: 10.1007/s00335-015-9590-y Abstract The development of ontologies for describing animal behavior has proved to be one of the most difficult of all scientific knowledge domains. Ranging from neurological processes to human emotions the range and scope needed for such ontologies is highly challenging, but if data integration and computational tools such as automated reasoning are to be fully applied in this important area the underlying principles of these ontologies need to

GFVO: the Genomic Feature and Variation Ontology

Applied Ontology genomics

Year: 2015 Venue: PeerJ Authors: Joachim Baran, Bibi Sehnaaz Begum Durgahee, Karen Eilbeck, Erick Antezana, Robert Hoehndorf, Michel Dumontier DOI: 10.7717/peerj.933 Abstract Falling costs in genomic laboratory experiments have led to a steady increase of genomic feature and variation data. Multiple genomic data formats exist for sharing these data, and whilst they are similar, they are addressing slightly different data viewpoints and are consequently not fully compatible with each other. The fragmentation of data format specifications makes it hard to integrate and interpret data for further

Ontology-based prediction of cancer driver genes

Applied Ontology Biomedical Informatics

Year: 2019 Venue: Scientific Reports Authors: Sara Althubaiti, Andreas Karwath, Ashraf Dallol, Adeeb Noor, Shadi Salem Alkhayyat, Rolina Alwassia, Katsuhiko Mineta, Takashi Gojobori, Andrew D Beggs, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity

Improving the classification of cardinality phenotypes using collections

Applied Ontology

Year: 2023 Venue: Journal of Biomedical Semantics Authors: Sarah M. Alghamdi, Robert Hoehndorf DOI: 10.1186/s13326-023-00290-y Abstract We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis. Topics Applied

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

Applied Ontology Ontology engineering

Year: 2019 Venue: Scientific Reports Authors: Sarah M. Alghamdi, Beth A. Sundberg, John P. Sundberg, Paul N. Schofield, Robert Hoehndorf Abstract Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but recently there has been a move to combine multiple ontologies to provide more powerful analytical possibilities. However, it is often not clear how to combine ontologies or how to assess or evaluate the potential design patterns available. Here we use a

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

Applied Ontology Ontology engineering

Year: 2020 Venue: Bioinformatics Authors: Fatima Z. Smaili, Xin Gao, Robert Hoehndorf Abstract Motivation: There are now over 500 ontologies in the life sciences. Over the past years, significant resources have been invested into formalizing these biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. At the same time, ontologies have extended their

Semantic similarity and machine learning with ontologies

Semantic similarity Applied Ontology Neuro-Symbolic AI

Year: 2020 Venue: Briefings in Bioinformatics Authors: Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf DOI: 10.1093/bib/bbaa199 Abstract 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

Applied Ontology

Applied ontology, in our group, means using formal representation to make complex phenotypes, functions, and processes amenable to computational analysis across domains. The starting point is the standardization and curation of biological knowledge using ontologies built with explicit logical commitments, and the long-term goal is to produce representations that support both human curators and automated reasoners. Earlier work in this line concentrated on foundational ontologies, including the General Formal Ontology (GFO) and its biological extension GFO-Bio, and on a formal ontology of

PhenomeNet

Applied Ontology Ontology engineering

Cross-species phenotype ontology and similarity network combining HPO, MPO, ZP and others; the substrate behind PhenomeNET-VP and DeepPheno. Get it GitHub: https://github.com/bio-ontology-research-group/phenomeblast ★ 1 Developed in projects CompleX: Variant Prioritization in Complex Disease Category: Ontologies & Resources

Units of Measurement Ontology (UO)

Applied Ontology Ontology engineering

OBO Foundry ontology of units of measurement; aligned with QUDT and used across biomedical data standards. Get it GitHub: https://github.com/bio-ontology-research-group/unit-ontology ★ 23 Category: Ontologies & Resources

mOWL Tutorial

Applied Ontology

Step-by-step worked notebooks that demonstrate every embedding family in mOWL on protein-function, gene-disease, and ontology-completion tasks. Get it GitHub: https://github.com/bio-ontology-research-group/mowl-tutorial ★ 8 Category: Teaching & Tutorials

Ontology Tutorial

Applied Ontology

Hands-on tutorial that walks new users through OWL, automated reasoning, and ontology-aware data analysis; basis for the AI in Biomedicine summer school. Get it GitHub: https://github.com/bio-ontology-research-group/ontology-tutorial ★ 73 Category: Teaching & Tutorials

Machine Learning with Ontologies

Applied Ontology

Companion code and worked examples for the Briefings in Bioinformatics tutorial review; the most-starred repository in the group. Get it GitHub: https://github.com/bio-ontology-research-group/machine-learning-with-ontologies ★ 132 Category: Teaching & Tutorials

vec2SPARQL

Applied Ontology Ontology engineering

Adds embedding-similarity functions to a SPARQL endpoint so that vector-space queries (k-nearest neighbours, cosine similarity) can be mixed with classical graph patterns. Get it GitHub: https://github.com/bio-ontology-research-group/vec2sparql ★ 14 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Reasoning & Tooling

OntoFunc

Applied Ontology Ontology engineering

Ontology-driven enrichment analysis that supports arbitrary OWL ontologies and full subsumption-aware aggregation, not only GO. Get it GitHub: https://github.com/bio-ontology-research-group/ontofunc Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Reasoning & Tooling

UNMIREOT

Applied Ontology Ontology engineering

Identifies, diagnoses, and semi-automatically repairs hidden contradictions and unsatisfiable classes introduced by partial imports (MIREOT) into biomedical ontologies. Get it GitHub: https://github.com/bio-ontology-research-group/UNMIREOT ★ 2 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Reasoning & Tooling

Onto2Graph

Applied Ontology Ontology engineering

Generates entailment-aware graph projections of OWL ontologies suitable for downstream graph machine learning while preserving the axioms' deductive structure. Get it GitHub: https://github.com/bio-ontology-research-group/Onto2Graph ★ 12 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Reasoning & Tooling

AberOWL

Applied Ontology Ontology engineering

Ontology repository delivering OWL EL reasoning as a service: stores hundreds of bio-ontologies, exposes SPARQL with class-expression query expansion, and powers semantic search over PubMed/PMC. Get it GitHub: https://github.com/bio-ontology-research-group/AberOWL ★ 10 Homepage: http://aber-owl.net Developed in projects Data integration and ontologies for microbial cell factories Category: Ontology Reasoning & Tooling

Interpretable Learning

Neuro-Symbolic AI Ontology engineering Applied Ontology

Generates interpretable symbolic rules from learned representations over biomedical knowledge bases. Get it GitHub: https://github.com/bio-ontology-research-group/interpretable-learning ★ 5 Developed in projects Towards sound, complete, and explainable machine learning with biomedical ontologies (CRG11) Category: Ontology Embedding & Machine Learning

EL2Box

Neuro-Symbolic AI Ontology engineering Applied Ontology

Box-shaped geometric embeddings for EL++ that strengthen the topological guarantees of EL Embeddings. Get it GitHub: https://github.com/bio-ontology-research-group/EL2Box_embedding ★ 2 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

Walking RDF and OWL

Neuro-Symbolic AI Ontology engineering Applied Ontology

Original feature-learning method over RDF graphs and OWL ontologies via biased random walks; the seed implementation for many later embedding methods including OWL2Vec*. Get it GitHub: https://github.com/bio-ontology-research-group/walking-rdf-and-owl ★ 47 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Embedding & Machine Learning

DL2Vec

Neuro-Symbolic AI Ontology engineering Applied Ontology

Encodes description-logic axioms as a directed graph and learns embeddings via random walks; widely used for downstream gene-disease and protein-function prediction. Get it GitHub: https://github.com/bio-ontology-research-group/DL2Vec ★ 20 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Embedding & Machine Learning

Onto2Vec

Neuro-Symbolic AI Ontology engineering Applied Ontology

Representation learning for ontologies and their annotations by treating logical axioms as natural-language sentences; predecessor of OPA2Vec. Get it GitHub: https://github.com/bio-ontology-research-group/onto2vec ★ 21 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Embedding & Machine Learning

OPA2Vec

Neuro-Symbolic AI Ontology engineering Applied Ontology

Combines ontology axioms with associated annotation properties (labels, synonyms, definitions) into a single corpus, then trains Word2Vec to produce semantically rich vectors for ontology classes. Get it GitHub: https://github.com/bio-ontology-research-group/opa2vec ★ 37 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences CompleX: Variant Prioritization in Complex Disease Category: Ontology Embedding & Machine Learning

DELE

Neuro-Symbolic AI Ontology engineering Applied Ontology

Deductive EL embeddings: enrich training data with the deductive closure of an ontology before learning, so embeddings recover entailment rather than only asserted axioms. Get it GitHub: https://github.com/bio-ontology-research-group/DELE ★ 2 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

catE

Neuro-Symbolic AI Ontology engineering Applied Ontology

Category-theoretic, lattice-preserving embedding of ALC description-logic ontologies that retains the consequence-closure semantics of the original theory. Get it GitHub: https://github.com/bio-ontology-research-group/catE ★ 3 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

EL Embeddings

Neuro-Symbolic AI Ontology engineering Applied Ontology

Reference implementation of geometric embeddings for the EL++ description logic, the predecessor of GeometrE and BoxSquaredEL. Preserves subsumption reasoning by mapping classes to convex regions. Get it GitHub: https://github.com/bio-ontology-research-group/el-embeddings ★ 28 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

mOWL

Neuro-Symbolic AI Ontology engineering Applied Ontology

Python library for machine learning with biomedical ontologies. Unifies projection-, axiom- and geometric-embedding methods (EL Embeddings, ELBE, BoxSquaredEL, OWL2Vec*, DL2Vec, OPA2Vec) behind one API, with first-class OWLAPI access and PyTorch integration. Get it GitHub: https://github.com/bio-ontology-research-group/mowl ★ 91 Homepage: https://mowl.readthedocs.io Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Towards sound, complete, and explainable machine learning with biomedical ontologies

Mahdi Bu Ali

Neuro-Symbolic AI Applied Ontology Ontology engineering

Mahdi Bu Ali completed his MSc in Computer Science at KAUST in 2025 under the supervision of Robert Hoehndorf. His research lay at the intersection of formal mathematics, automated theorem proving, and large language models. His thesis, Automated Theorem Proving with Large Language Models in Lean: An Exploration of Specialized In-Context Learning and General-Purpose Hierarchical Architectures, addresses the long-standing goal of automating mathematical reasoning in interactive theorem provers such as Lean. Automated theorem proving is constrained both by the cost of exploring vast proof search

Knowledge Representation and Reasoning (2015-2016)

Ontology engineering Applied Ontology

Year: 2015-2016 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology

Applied Ontology (2016-2017)

Applied Ontology Ontology engineering

Year: 2016-2017 Code: CS 390EE Program: Computer Science Institution: KAUST Role: Instructor Topics: Applied Ontology, Ontology engineering

Knowledge Representation and Reasoning (2017-2018)

Ontology engineering Applied Ontology

Year: 2017-2018 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology

Applied Ontology (2018)

Applied Ontology Ontology engineering

Year: 2018 Code: CS 322 Program: Computer Science Institution: KAUST Role: Instructor Topics: Applied Ontology, Ontology engineering

Knowledge Representation and Reasoning (2020)

Ontology engineering Applied Ontology Neuro-Symbolic AI

Year: 2020 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Knowledge Representation and Reasoning (2021)

Ontology engineering Applied Ontology Neuro-Symbolic AI

Year: 2021 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Knowledge Representation and Reasoning (2022)

Ontology engineering Applied Ontology Neuro-Symbolic AI

Year: 2022 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Neurosymbolic AI (2024)

Neuro-Symbolic AI Applied Ontology

Year: 2024 Code: CS 394D Program: Computer Science Institution: KAUST Role: Instructor Topics: Neuro-symbolic AI, Applied Ontology

Knowledge Representation and Reasoning (2024)

Ontology engineering Applied Ontology Neuro-Symbolic AI

Year: 2024 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Neurosymbolic AI (2026)

Neuro-Symbolic AI Applied Ontology

Year: 2026 Code: CS 394D Program: Computer Science Institution: KAUST Role: Instructor Topics: Neuro-symbolic AI, Applied Ontology

Knowledge Representation and Reasoning (2026)

Ontology engineering Applied Ontology Neuro-Symbolic AI

Year: 2026 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Data integration and ontologies for microbial cell factories

Mon, May 18 2026

Applied Ontology Drug mechanisms Microbial communities Neuro-Symbolic AI

Industrial biotechnology depends on integrating data from sources that were never designed to be combined: enzyme and reaction databases, protein-family resources, pathway maps, strain collections, phenotypic screens, and the experimental literature itself. Each uses different identifiers, different vocabularies, and different levels of granularity, and naive integration produces brittle pipelines that break whenever a source is updated. The Microbial Cell Factories initiative at KAUST's Computational Bioscience Research Center (CBRC, now dissolved), led by Vladimir Bajic with Hoehndorf as CoI

Bio2Vec: Smart analytics infrastructure for the life sciences

Mon, May 18 2026

Applied Ontology Neuro-Symbolic AI Semantic similarity

By the mid-2010s the life sciences had produced an extraordinary investment in machine-readable knowledge: biomedical ontologies were used throughout biology to annotate data, and large RDF knowledge graphs such as Bio2RDF aggregated billions of statements from dozens of major databases. At the same time, large personal genomic datasets, the UK 100,000 Genomes project, UK Biobank, and the Saudi Human Genome Program, were coming online, and translating these into clinical insight depended on integrating them with that existing background knowledge. Generic knowledge-graph machine learning

Improvement of genetic variant prioritization technology

Mon, May 18 2026

Applied Ontology Neuro-Symbolic AI Rare disease

By 2018 the PhenomeNET Variant Predictor (PVP) had been validated as a research tool for prioritizing causative variants in Mendelian disease, and a clear question had emerged: could the same phenotype-driven, knowledge-graph-based machinery be retargeted to cancer, where the variant landscape is somatic, heterogeneous, and dominated by combinations of driver and passenger mutations rather than single pathogenic alleles? This one-year KAUST Center Partnership Fund project (2018–2019, OSR-2018-CPF-3657-0; with Schofield in Cambridge, Gkoutos in Birmingham, and Bajic at KAUST) was the

CompleX: Variant Prioritization in Complex Disease

Mon, May 18 2026

Applied Ontology Neuro-Symbolic AI Rare disease Semantic similarity

The hardest cases in clinical genome sequencing are the ones where no single variant explains the disease. As Mendelian gene discovery slows and the diagnostic rate for whole-exome sequencing stalls below 50%, growing evidence points to oligogenic and polygenic origins: combinations of medium-rare or common alleles that, individually, look unremarkable. Population-level approaches lack the power to find them, and traditional single-gene Mendelian reasoning ignores them. The CompleX project (2019–2021, with the Universities of Cambridge and Birmingham) set out to break this impasse by extending

IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information

Mon, May 18 2026

Applied Ontology Neuro-Symbolic AI Rare disease

Biological measurements are inherently high-dimensional and heterogeneous: omics platforms produce thousands to millions of features per individual, and they coexist with qualitative information such as diagnoses, phenotype calls, and prescriptions. Biomedical ontologies and knowledge graphs encode rich qualitative background knowledge, but they are largely disconnected from the quantitative measurements that gave rise to the categorical phenotypes in the first place. Conversely, graph neural networks and other methods that handle quantitative data on graphs do not yet exploit the formal

Computational methods for functional metagenomics: from protein functions to multi-scale interactions

Mon, May 18 2026

Applied Ontology Microbial communities Neuro-Symbolic AI protein function

Metagenomic sequencing has made it routine to read the DNA of an entire microbial community, but most analysis pipelines stop at taxonomic composition or at the level of individual protein families. The really biologically informative questions, which proteins do what, which proteins interact, which metabolic pathways are reconstructible, and how the community as a whole interacts with its environment or host, remain largely out of reach computationally. Even associations that are very robust empirically, for example between gut microbiome composition and colorectal cancer or inflammatory

Towards sound, complete, and explainable machine learning with biomedical ontologies (CRG11)

Sun, Jan 1 2023 - Thu, Jan 1 2026

Applied Ontology Neuro-Symbolic AI Ontology engineering Semantic similarity

Deep learning has driven much of the recent progress in bioinformatics, but the resulting models are essentially black boxes: it is hard or impossible to ask why a prediction was made, to verify it against existing knowledge, or to detect biases that emerge from the interaction of dataset, architecture, and training objective. In clinical and biomedical settings these limitations matter, both because clinicians need to trust the systems they use and because formal guarantees of soundness and fairness can only be obtained when the inferential machinery can be inspected. The grand challenge