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Hyaline Arteriolosclerosis in 30 Strains of Aged Inbred Mice

Year: 2019 Venue: Veterinary Pathology Authors: Timothy K. Cooper, Kathleen A. Silva, Victoria E. Kennedy, Sarah M. Alghamdi, Robert Hoehndorf, Beth A. Sundberg, Paul N. Schofield, John P. Sundberg DOI: 10.1177/0300985819844822 Abstract During a screen for vascular phenotypes in aged laboratory mice, a unique discrete phenotype of hyaline arteriolosclerosis of the intertubular arteries and arterioles of the testes was identified in several inbred strains. Lesions were limited to the testes and did not occur as part of any renal, systemic, or pulmonary arteriopathy or vasculitis phenotype

Improving the classification of cardinality phenotypes using collections

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

In silico exploration of Red Sea Bacillus genomes for natural product biosynthetic gene clusters

Year: 2018 Venue: BMC Genomics Authors: Ghofran Othoum, Salim Bougouffa, Rozaimi Razali, Ameerah Bokhari, Soha Alamoudi, Andr\'e Antunes, Xin Gao, Robert Hoehndorf, Stefan T. Arold, Takashi Gojobori, Heribert Hirt, Ivan Mijakovic, Vladimir B. Bajic, Feras F. Lafi, Magbubah Essack Abstract The increasing spectrum of multidrug-resistant bacteria is a major global public health concern, necessitating discovery of novel antimicrobial agents. Here, members of the genus Bacillus are investigated as a potentially attractive source of novel antibiotics due to their broad spectrum of antimicrobial

In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria

Year: 2017 Venue: BMC Genomics Authors: Olaa Motwalli, Magbubah Essack, Boris R. Jankovic, Boyang Ji, Xinyao Liu, Hifzur Rahman Ansari, Robert Hoehndorf, Xin Gao, Stefan T. Arold, Katsuhiko Mineta, John A. C. Archer, Takashi Gojobori, Ivan Mijakovic, Vladimir B. Bajic DOI: 10.1186/s12864-016-3389-4 Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular

INDIGENA: inductive prediction of disease–gene associations using phenotype ontologies

Year: 2026 Venue: Bioinformatics Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1093/bioinformatics/btag325 Abstract MOTIVATION: Predicting gene-disease associations (GDAs) can be framed as a ranking problem where genes are ranked for a query disease based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can

Integrating phenotype ontologies with PhenomeNET

Year: 2016 Venue: Proceedings of Ontology Matching Workshop 2016 Authors: Miguel Rodriguez-Garcia, Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract PhenomeNET is a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Here, we apply the PhenomeNET to identify related classes from four phenotype and

Interactively Exploring Graph Coloring Algorithms in a Bilingual Web Platform with Gamification

Year: 2017 Venue: Proceedings of EdMedia: World Conference on Educational Media and Technology 2017 Authors: Maha Alrashed, Lujain Alharbi, Omamah Talal Al-Muhammadi, Salha Bahadiq, Robert Hoehndorf, Liam Mencel Abstract Graph coloring is a concept in graph theory that has many real world applications, such as scheduling and map coloring, thus making it an essential part of a computer science curriculum. Most graph theory courses are taught using standard methods such as with textbooks or a blackboard. Such methods introduce graph theory without providing the student with an adequate

Klarigi: Characteristic explanations for semantic biomedical data

Year: 2023 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, John A. Williams, Paul N. Schofield, Sophie Russell, Samantha C. Pendleton, Andreas Karwath, Hilary Fanning, Simon Ball, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2022.106425 Abstract Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such

Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction

Year: 2024 Venue: Neural-Symbolic Learning and Reasoning Authors: Yasir Ghunaim, Robert Hoehndorf DOI: 10.1007/978-3-031-71170-1_10 Abstract Pre-training machine learning models on molecular properties has proven effective for generating robust and generalizable representations, which is critical for advancements in drug discovery and materials science. While recent work has primarily focused on data-driven approaches, the KANO model introduces a novel paradigm by incorporating knowledge-enhanced pre-training. In this work, we expand upon KANO by integrating the large-scale ChEBI knowledge

Large-Scale Reasoning over Functions in Biomedical Ontologies

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

Lattice-Based ALC Ontology Embeddings With Saturation

Year: 2025 Venue: Neurosymbolic Artificial Intelligence Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1177/29498732251340186 Abstract Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies are expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a graph out of ontologies, neglecting the semantics of the logic therein. Recent semantic

Lattice-Preserving ALC Ontology Embeddings

Year: 2024 Venue: Neural-Symbolic Learning and Reasoning Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1007/978-3-031-71167-1_19 Abstract Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies is expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a graph out of ontologies, neglecting the semantics of the logic therein. Recent semantic

LEP-AD: language embedding of proteins and attention to drugs predicts drug-target interactions

Year: 2026 Venue: Journal of Cheminformatics Authors: Reem Alsulami, Robert Lehmann, Anuj Daga, Sumeer A. Khan, Raik Gr\"unberg, Ahmed Abogosh, David Gomez Cabrero, Stefan T. Arold, Robert Hoehndorf, Jesper Tegner, Narsis A. Kiani DOI: 10.1186/s13321-026-01167-9 Abstract INTRODUCTION: Predicting drug-target interactions remains a significant challenge in drug development and lead optimization. Recent advances have leveraged machine learning algorithms to model drug-target interactions from molecular and sequence data. MATERIALS AND METHODS: In this work, we use Evolutionary Scale Modeling (ESM

Media coverage

Selected press coverage and recorded talks and interviews about the Bio-Ontology Research Group and its work. In the news 34 Talks & videos 10 AI tool maps hidden links between diseases 2025 · KAUST CEMSE · Read the paper → Coverage of a method that mines causal relationships between diseases from the literature and uses them to improve polygenic risk scores. New genetic maps expected to improve personalized medicine for underrepresented populations 2025 · KAUST News · Read the paper → KAUST coverage of JaSaPaGe, a population-specific pangenome reference for Saudi and Japanese populations

Microbial communities

Microbial communities drive most of the biogeochemical and biomedical processes that sustain life, yet their functional repertoire remains poorly characterized: metagenomic samples are dominated by proteins with no close homolog in curated databases, and most prediction tools have been trained on eukaryotic sequences. Our work in this area lifts single-protein function prediction up to the level of whole microbial communities by combining ontology-aware deep learning with multi-scale systems analysis. The distinctive angle is to treat metagenomes not as bags of genes but as functional systems

Molecular basis and cellular effects of Janus-class–driven cytoplasmic PYK2 coacervates

Year: 2026 Venue: Communications Biology Authors: Giovanni Colombo, Israa Salem, Kacper Szczepski, Piao Yu, Shaden Alfaiyz, Francisco Javier Guzman-Vega, Ahmed Abogosh, Maxat Kulmanov, Samah Al-Harthi, Gress Kadare, Robert Hoehndorf, Jean-Antoine Girault, Łukasz Jaremko, Afaque A. Momin, Stefan T. Arold DOI: 10.1038/s42003-025-09463-0 Abstract Kinase activity is increasingly linked to biomolecular phase separation. Focal adhesion kinase (FAK) forms membrane-associated condensates with paxillin to promote adhesion. Here we show that its paralogue, proline-rich tyrosine kinase 2 (PYK2)

mOWL

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

Multi-faceted semantic clustering with text-derived phenotypes

Year: 2021 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, John A. Williams, Andreas Karwath, Hilary Fanning, Simon Ball, Paul N. Schofield, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2021.104904 Abstract 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

Nail abnormalities identified in an ageing study of 30 inbred mouse strains

Year: 2019 Venue: Experimental Dermatology Authors: Sarah C. Linn, Allison M. Mustonen, Kathleen A. Silva, Victoria E. Kennedy, Beth A. Sundberg, Lesley S. Bechtold, Sarah M. Alghamdi, Robert Hoehndorf, Paul N. Schofield, John P. Sundberg DOI: 10.1111/exd.13759 Abstract In a large-scale ageing study, 30 inbred mouse strains were systematically screened for histologic evidence of lesions in all organ systems. Ten strains were diagnosed with similar nail abnormalities. The highest frequency was noted in NON/ShiLtJ mice. Lesions identified fell into two main categories: acute to chronic

Nanodesigner: resolving the complex-CDR interdependency with iterative refinement

Year: 2025 Venue: Journal of Cheminformatics Authors: Melissa Maria Rios Zertuche, Senay Kafkas, Dominik Renn, Magnus Rueping, Robert Hoehndorf DOI: 10.1186/s13321-025-01069-2 Abstract Abstract Camelid heavy-chain only antibodies consist of two heavy chains and single variable domains (VHHs), which retain antigen-binding functionality even when isolated. The term “nanobody” is now more generally used for describing small, single-domain antibodies. Several antibody generative models have been developed for the sequence and structure co-design of the complementarity-determining regions (CDRs)

Neural Multi-hop Logical Query Answering with Concept-Level Answers

Year: 2023 Venue: The Semantic Web – ISWC 2023 Authors: Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf Abstract Neural multi-hop logical query answering ({LQA}) is a fundamental task to explore relational data such as knowledge graphs, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers. Although previous {LQA} methods can give specific instance-level answers, they are not able to provide descriptive concept-level answers, where each concept is a description of a set of instances

Neuro-symbolic AI

Neuro-symbolic methods in bioinformatics aim to combine the deductive guarantees of symbolic knowledge with the inductive power of statistical learning. Our group develops methods that map entities described in formal ontologies into vector spaces while preserving the semantic relations expressed by their axioms, so that downstream models can use background knowledge directly in similarity search, link prediction, and classification. The distinctive angle at KAUST is a focus on description logics as the source of structure: we design embedding constructions for languages such as EL++ and ALC

Neuro-Symbolic AI in Life Sciences

Year: 2025 Venue: Handbook on Neurosymbolic AI and Knowledge Graphs Authors: Robert Hoehndorf, Catia Pesquita, Fernando Zhapa-Camacho DOI: 10.3233/faia250239 Abstract Life sciences have a long history of driving advancements in various disciplines, including mathematics, philosophy, and logic. In recent years, life sciences have also become a significant application area for Artificial Intelligence (AI) technologies, including for neuro-symbolic AI methods. The life sciences knowledge infrastructure, characterized by its widespread use of ontologies, complex annotation models, large size, and

Neuro-symbolic representation learning on biological knowledge graphs

Year: 2017 Venue: Bioinformatics Authors: Mona Alshahrani, Mohammad Asif Khan, Omar Maddouri, Akira R. Kinjo, Nuria Queralt-Rosinach, Robert Hoehndorf Abstract Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on

Notions of similarity for systems biology models

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

OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

Year: 2018 Venue: Scientific Reports Authors: Imane Boudellioua, Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Purpose: An increasing number of Mendelian disorders have been identified for which two or more variants in one or more genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of variants underlying oligogenic diseases in individual whole exome or whole

Onto2Graph

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

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

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

Ontology based text mining of gene-phenotype associations: application to candidate gene prediction

Year: 2019 Venue: Database Authors: Senay Kafkas, Robert Hoehndorf Abstract Gene–phenotype associations play an important role in understanding the disease mechanisms which is a requirement for treatment development. A portion of gene–phenotype associations are observed mainly experimentally and made publicly available through several standard resources such as MGI. However, there is still a vast amount of gene–phenotype associations buried in the biomedical literature. Given the large amount of literature data, we need automated text mining tools to alleviate the burden in manual curation of

Ontology Embedding: A Survey of Methods, Applications and Resources

Year: 2025 Venue: IEEE Transactions on Knowledge and Data Engineering Authors: Jiaoyan Chen, Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf, Yuan He, Ian Horrocks DOI: 10.1109/tkde.2025.3559023 Abstract Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoning that ontologies can directly support are quite limited in learning, approximation and prediction. One straightforward solution is to integrate statistical analysis

Ontology engineering

Ontology engineering and semantic interoperability address the practical problem of turning hundreds of independently developed biomedical ontologies into an infrastructure that can be queried, reasoned over, and combined at scale. Our group designs architectures for processing large, heterogeneous datasets using Semantic Web standards, with a particular emphasis on bringing automated reasoning into routine data-access workflows. The angle taken at KAUST is engineering-led: we treat ontology-based data access as a service that must be fast enough for interactive use, expressive enough to

Ontology-based prediction of cancer driver genes

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

Ontology-based validation and identification of regulatory phenotypes

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