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OPA2Vec

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

OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction

Year: 2018 Venue: Bioinformatics Authors: Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf DOI: 10.1093/bioinformatics/bty933 Abstract Motivation: Ontologies are widely used in biology for data annotation, integration and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally

PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

Year: 2019 Venue: Scientific Data Authors: Senay Kafkas, Marwa Abdelhakim, Yasmeen Hashish, Maxat Kulmanov, Marwa Abdellatif, Paul N Schofield, Robert Hoehndorf DOI: 10.1101/489971 Abstract Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the

Phased genome assemblies and pangenome graphs of human populations of Japan and Saudi Arabia

Year: 2025 Venue: Scientific Data Authors: Maxat Kulmanov, Saeideh Ashouri, Yang Liu, Marwa Abdelhakim, Ebtehal Alsolme, Masao Nagasaki, Yasuyuki Ohkawa, Yutaka Suzuki, Rund Tawfiq, Katsushi Tokunaga, Toshiaki Katayama, Malak S. Abedalthagafi, Robert Hoehndorf, Yosuke Kawai DOI: 10.1038/s41597-025-05652-y Abstract The selection of a reference sequence in genome analysis is critical, as it serves as the foundation for all downstream analyses. Recently, the pangenome graph has been proposed as a data model that incorporates haplotypes from multiple individuals. Here we present JaSaPaGe, a

Phenotype informatics

Phenotypes are the observable consequences of genotype, environment, and their interaction, and they remain the principal currency by which disease is recognized, model organisms are characterized, and plant traits are cataloged. Our work develops the informatics infrastructure that makes phenotype data computable across species and clinical settings: the phenotype ontologies themselves, the cross-species crosswalks that link them, the tools that capture and standardize phenotype descriptions from text and images, and the computational pipelines that connect phenotype evidence back to genes

Phenotype-driven discovery of digenic variants in personal genome sequences

Year: 2017 Venue: Proceedings of VarI-SIG Authors: Imane Boudellioua, Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Identification of variants associated with inherited diseases is a major challenge, in particular in the analysis of clinical sequence data from individual patients. An increasing number of Mendelian diseases have been identified in which two or more variants in multiple genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. Information

Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion

Year: 2022 Venue: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Authors: Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang DOI: 10.24963/ijcai.2022/312 Abstract Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph completion (KGC). However, existing KGC methods suffer from two main issues, 1) the false negative issue, i.e., the sampled negative

Predicting protein functions using positive-unlabeled ranking with ontology-based priors

Year: 2024 Venue: Bioinformatics Authors: Fernando Zhapa-Camacho, Zhenwei Tang, Maxat Kulmanov, Robert Hoehndorf DOI: 10.1093/bioinformatics/btae237 Abstract Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive

Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

Year: 2016 Venue: PLoS ONE Authors: Imane Boudellioua, Rabie Saidi, Robert Hoehndorf, Maria J. Martin, Victor Solovyev DOI: 10.1371/journal.pone.0158896 Abstract The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein

Projects

Funded research projects led by or involving the Bio-Ontology Research Group. Period Project Role 2025–2026 Personalized cancer treatment prediction (KCSH Pathway to Impact 2025) PI 2024–ongoing KAUST Center of Excellence for Generative AI (Health and Wellness, BCB theme) PI 2024–2026 A public Saudi pangenome as reference for genomics in the Middle East PI 2024–2026 KAUST Center of Excellence for Smart Health — BCB research theme (Infectious Disease area) Area lead (Infectious Disease), BCB theme 2023–2026 Towards sound, complete, and explainable machine learning with biomedical ontologies

Protein function

Determining what a protein does, from its sequence alone, is one of the foundational problems of computational molecular biology. Experimental characterization cannot keep pace with sequencing throughput, and large-scale ontologies such as the Gene Ontology (GO) provide the structured background knowledge needed to make automated assignment of function tractable. Our work centres on the DeepGO family of systems, which couple deep neural networks with the formal axioms of the Gene Ontology so that predicted annotations are not only accurate but also logically consistent with what is already

Protein function prediction as approximate semantic entailment

Year: 2024 Venue: Nature Machine Intelligence Authors: Maxat Kulmanov, Francisco J. Guzman-Vega, Paula Duek Roggli, Lydie Lane, Stefan T. Arold, Robert Hoehndorf DOI: 10.1038/s42256-024-00795-w Abstract Abstract The Gene Ontology (GO) is a formal, axiomatic theory with over 100,000 axioms that describe the molecular functions, biological processes and cellular locations of proteins in three subontologies. Predicting the functions of proteins using the GO requires both learning and reasoning capabilities in order to maintain consistency and exploit the background knowledge in the GO. Many

Publications

A complete list of publications by members of the Bio-Ontology Research Group, drawn from our local research knowledge graph (223 entries). Quick filters: Neuro-symbolic AI Ontology engineering Applied Ontology Protein function Rare disease Drug mechanisms Genomics Biomedical informatics Semantic similarity Microbial communities Phenotype informatics Bioengineering Publications by topic Neuro-symbolic AI (29) (2025) Ontology Embedding: A Survey of Methods, Applications and Resources (2025) Neuro-Symbolic AI in Life Sciences (2024) Predicting protein functions using positive-unlabeled ranking

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

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

Ranking Adverse Drug Reactions With Crowdsourcing

Year: 2015 Venue: J Med Internet Res Authors: Assaf Gottlieb, Robert Hoehndorf, Michel Dumontier, B. Russ Altman DOI: 10.2196/jmir.3962 Abstract Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based

Rare disease

The diagnosis of rare and Mendelian disease has been transformed by exome and genome sequencing, but interpretation remains the bottleneck: a typical patient genome contains tens of thousands of rare variants, only one or a few of which are causative. Effective diagnostic support requires the integration of patient-specific molecular data with structured background knowledge about genes, phenotypes, and disease mechanisms. We develop methods, anchored on the PhenomeNET phenotype network and the PVP family of variant prioritization tools, that combine automated reasoning over phenotype

Research

BORG works on biomedical ontologies, neuro-symbolic AI, disease and phenotype informatics, and protein-function prediction. The tabs below cover the research topics we work on, the funded projects that support that work, and the open-source software the group produces. Topics 12 Projects 21 Software 43 Research topics the Bio-Ontology Research Group works on. Open a topic for the full overview, related projects, software, and publications. Neuro-symbolic AI We work on methods that integrate symbolic knowledge with statistical learning. This includes mapping entities in formal ontologies into

Robust Knowledge Graph Embedding via Denoising

Year: 2026 Venue: The Semantic Web -- ESWC 2026 Authors: Tengwei Song, Xudong Ma, Yang Liu, Jie Luo, Robert Hoehndorf DOI: 10.1007/978-3-032-25156-5_22 Abstract Knowledge graph embedding models have achieved remarkable success in link prediction and reasoning tasks, yet they remain highly vulnerable to perturbations in the embedding space. Such perturbations, whether introduced by noisy triples, representation drift or adversarial manipulation, can lead to severe degradation in prediction stability and significantly affect downstream multi-hop reasoning processes. To address this challenge, we

Sa1216: Development of colorectal cancer and matched healthy organoids from Saudi patients: a case study

Year: 2025 Venue: Gastroenterology Authors: Dana Alhattab, Duna Barakeh, Basma Khoja, Ahmad Elhadi, Jameel Miro, Saleh A. Alessy, Ahmed Alharbi, Manal Bokhary, May Alzahrani, Saga Ali, Wadha Almohamdi, Lama Hefni, Manola Moretti, Yang Liu, Marwa Abdelhakim, Abeer Abdullah, Waleed Alomaim, Robert Hoehndorf, Charlotte Hauser, Saleh A. Alqahtani DOI: 10.1016/s0016-5085(25)01866-9 Topics Bioengineering · Biomedical informatics

Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

Year: 2018 Venue: Bioinformatics Authors: Mona Alshahrani, Robert Hoehndorf Abstract Motivation: In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease's (or patient's) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well

Semantic prioritization of novel causative genomic variants

Year: 2017 Venue: PLOS Computational Biology Authors: Imane Boudellioua, Rozaimi B. Mahamad Razali, Maxat Kulmanov, Yasmeen Hashish, Vladimir B. Bajic, Eva Goncalves-Serra, Nadia Schoenmakers, Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf DOI: 10.1371/journal.pcbi.1005500 Abstract Author summary We address the problem of how to distinguish which of the many thousands of DNA sequence variants carried by an individual with a rare disease is responsible for the disease phenotypes. This can help clinicians arrive at a diagnosis, but also can be instrumental in improving our

Semantic similarity

Semantic similarity measures over biomedical ontologies sit at the core of several of our research lines, from disease-gene prioritization to ontology-aware function transfer and biodiversity knowledge-graph search. We develop, benchmark and apply measures that exploit the OWL axiomatic structure of an ontology rather than only its lexical or taxonomic skeleton, and we have repeatedly shown that this richer semantics translates into measurable improvements on downstream prediction tasks. Operating within KAUST's Computer Science Program, our distinctive contribution is that we treat similarity

Semantic similarity and machine learning with ontologies

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

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

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

Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference

Year: 2019 Venue: The World Wide Web Conference Authors: Shichao Pei, Lu Yu, Robert Hoehndorf, Xiangliang Zhang DOI: 10.1145/3308558.3313646 Abstract Entity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection. Most of the recent solutions for entity alignment are based on knowledge graph embedding, which maps knowledge entities in a low-dimension space where entities are connected with the guidance of prior aligned entity pairs. The study in this paper focuses on two

SIDEKICK: A Semantically Integrated Resource for Drug Effects, Indications, and Contraindications

Year: 2026 Venue: The Semantic Web -- ESWC 2026 Authors: Mohammad Ashhad, Olga Mashkova, Ricardo Henao, Robert Hoehndorf DOI: 10.1007/978-3-032-25159-6_14 Abstract Pharmacovigilance and clinical decision support systems utilize structured drug safety data to guide medical practice. However, existing datasets frequently depend on terminologies such as MedDRA, which limits their semantic reasoning capabilities and their interoperability with Semantic Web ontologies and knowledge graphs. To address this gap, we developed SIDEKICK, a knowledge graph that standardizes drug indications

Similarity-based search of model organism, disease and drug effect phenotypes

Year: 2015 Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Michael Gruenberger, Georgios Gkoutos, Paul Schofield DOI: 10.1186/s13326-015-0001-9 Abstract BACKGROUND:Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity.RESULTS:We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of

SPARQL2OWL: Towards Bridging the Semantic Gap Between RDF and OWL

Year: 2016 Venue: Proceedings of the Joint International Conference on Biological Ontology and BioCreative, Corvallis, Oregon, United States, August 1-4, 2016. Authors: Mona Alshahrani, Hussein Almashouq, Robert Hoehndorf Abstract Several large databases in biology are now making theirinformation available through the Resource Description Framework(RDF). RDF can be used for large datasets and provides agraph-based semantics. The Web Ontology Language (OWL),another Semantic Web standard, provides a more formal, model-theoretic semantics. While some approaches combine RDF andOWL, for example for

Starvar: symptom-based tool for automatic ranking of variants using evidence from literature and genomes

Year: 2023 Venue: BMC Bioinformatics Authors: Senay Kafkas, Marwa Abdelhakim, Mahmut Uludag, Azza Althagafi, Malak Alghamdi, Robert Hoehndorf DOI: 10.1186/s12859-023-05406-w Abstract Abstract Background Identifying variants associated with diseases is a challenging task in medical genetics research. Current studies that prioritize variants within individual genomes generally rely on known variants, evidence from literature and genomes, and patient symptoms and clinical signs. The functionalities of the existing tools, which rank variants based on given patient symptoms and clinical signs, are

Su1295: Chemically defined peptide-based matrices enabling the development of colorectal organoid models for therapeutic applications and disease modeling

Year: 2025 Venue: Gastroenterology Authors: Dana Alhattab, Duna Barakeh, Basma Khoja, Ahmad Elhadi, Jameel Miro, Saleh A. Alessy, Ahmed Alharbi, Manal Bokhary, May Alzahrani, Saga Ali, Wadha Almohamdi, Lama Hefni, Manola Moretti, Abeer Abdullah, Waleed Alomaim, Robert Hoehndorf, Charlotte Hauser, Saleh A. Alqahtani DOI: 10.1016/s0016-5085(25)02643-5 Topics Bioengineering · Drug mechanisms

Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

Year: 2018 Venue: Botany Letters Authors: Sohaib Younis, Claus Weiland, Robert Hoehndorf, Stefan Dressler, Thomas Hickler, Bernhard Seeger, Marco Schmidt Abstract Herbaria worldwide are housing a treasure of hundreds of millions of herbarium specimens, which are increasingly being digitized and thereby more accessible to the scientific community. At the same time, deep-learning algorithms are rapidly improving pattern recognition from images and these techniques are more and more being applied to biological objects. In this study, we are using digital images of herbarium specimens in order to

Teaching

Robert Hoehndorf teaches at KAUST in the Computer Science Program and supervises PhD and MSc theses. The Courses tab lists CS courses he has taught since 2015; the Theses tab lists every student thesis produced in the group. Courses 22 Theses 33 Courses taught by Robert Hoehndorf — recent first. Year Course Program Role Code 2026 Knowledge Representation and Reasoning Computer Science Instructor CS 213 2026 Neurosymbolic AI Computer Science Instructor CS 394D 2025 Application of AI in Bioinformatics Computer Science Instructor CS 321 2025 Algorithms in Bioinformatics Computer Science

The anatomy of phenotype ontologies: principles, properties and applications

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

The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients

Year: 2025 Venue: Scientific Reports Authors: Senay Kafkas, Marwa Abdelhakim, Azza Althagafi, Sumyyah Toonsi, Malak Alghamdi, Paul N. Schofield, Robert Hoehndorf DOI: 10.1038/s41598-025-99539-y Abstract Computational methods for identifying gene-disease associations can use both genomic and phenotypic information to prioritize genes and variants that may be associated with genetic diseases. Phenotype-based methods commonly rely on comparing phenotypes observed in a patient with databases of genotype-to-phenotype associations using measures of semantic similarity. They are constrained by the

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Year: 2019 Venue: Genome Biology Authors: Naihui Zhou, Yuxiang Jiang, Timothy R Bergquist, Alexandra J Lee, Balint Z Kacsoh, Alex W Crocker, Kimberley A Lewis, George Georghiou, Huy N Nguyen, Md Nafiz Hamid, Larry Davis, Tunca Dogan, Volkan Atalay, Ahmet S Rifaioglu, Alperen Dalkiran, Rengul Cetin-Atalay, Chengxin Zhang, Rebecca L Hurto, Peter L Freddolino, Yang Zhang, Prajwal Bhat, Fran Supek, Jos\'e M Fern\'andez, Branislava Gemovic, Vladimir R Perovic, Radoslav S Davidovi\'c, Neven Sumonja, Nevena Veljkovic, Ehsaneddin Asgari, Mohammad RK Mofrad, Giuseppe Profiti, Castrense Savojardo, Pier

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

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 Impact of Mechanical Cues on the Metabolomic and Transcriptomic Profiles of Human Dermal Fibroblasts Cultured in Ultrashort Self-Assembling Peptide 3D Scaffolds

Year: 2023 Venue: ACS Nano Authors: Sherin Abdelrahman, Rui Ge, Hepi H. Susapto, Yang Liu, Faris Samkari, Manola Moretti, Xinzhi Liu, Robert Hoehndorf, Abdul-Hamid Emwas, Mariusz Jaremko, Ranim H. Rawas, Charlotte A. E. Hauser DOI: 10.1021/acsnano.3c01176 Abstract Cells' interactions with their microenvironment influence their morphological features and regulate crucial cellular functions including proliferation, differentiation, metabolism, and gene expression. Most biological data available are based on in vitro two-dimensional (2D) cellular models, which fail to recapitulate the

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

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

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

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