Pages

Other

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

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

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

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

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 32 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

Text-mining solutions for biomedical research: enabling integrative biology

Year: 2012 Venue: Nature Reviews Genetics Authors: Dietrich Rebholz-Schuhmann, Anika Oellrich, Robert Hoehndorf Abstract In response to the unbridled growth of information in literature and biomedical databases, researchers require efficient means of handling and extracting information. As well as providing background information for research, scientific publications can be processed to transform textual information into database content or complex networks and can be integrated with existing knowledge resources to suggest novel hypotheses. Information extraction and text data analysis can be

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 RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions.

Venue: BMC Research Notes Authors: Bernard de Bono, Robert Hoehndorf, Sarala Wimalaratne, Georgios V. Gkoutos, Pierre Grenon Abstract BACKGROUND:The practice and research of medicine generates considerable quantities of data and model resources (DMRs). Although in principle biomedical resources are re-usable, in practice few can currently be shared. In particular, the clinical communities in physiology and pharmacology research, as well as medical education, (i.e. PPME communities) are facing considerable operational and technical obstacles in sharing data and models.FINDINGS:We outline the

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

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

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

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

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

Thematic series on biomedical ontologies in JBMS: challenges and new directions

Year: 2014 Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Melissa Haendel, Robert Stevens, Dietrich Rebholz-Schuhmann Abstract Over the past 15 years, the biomedical research community has increased its efforts to produce ontologies encoding biomedical knowledge, and to provide the corresponding infrastructure to maintain them. As ontologies are becoming a central part of biological and biomedical research, a communication channel to publish frequent updates and latest developments on them would be an advantage.Here, we introduce the JBMS thematic series on Biomedical

Theses

Theses produced by BORG members at KAUST. Author names link to their BORG profile. PhD theses Yang Liu (2026, Bioengineering) — Reference Bias and Variant Interpretation in Human Disease Genomics — defended 2026-04-27 Fernando Zhapa-Camacho (2026, Computer Science) — Neuro-symbolic methods for embedding ontologies, and applications in life sciences — defended 2026-04-26 Sumyyah Toonsi (2025, Computer Science) — Data Driven Mining of Causal Disease Relations to Enhance Disease Centric Predictions — defended 2025-05-15 Rund Tawfiq (2025, Bioengineering) — Computational Methods for Functional

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

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

Topics

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 vector spaces while preserving their semantic relations. We develop embedding frameworks for Description Logics (e.g., EL++ and ALC) that provide mathematical guarantees for logical soundness and approximate the interpretation of formalized theories. ontology embeddings description logic geometric

Towards Improving Phenotype Representation in OWL

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

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

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

Towards similarity-based differential diagnostics for common diseases

Year: 2021 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, Andreas Karwath, John A. Williams, Sophie Russell, Silver Makepeace, Alexander Carberry, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2021.104360 Abstract Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied

Updating the CEMO ontology for future epidemiological challenges

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

Usage of cell nomenclature in biomedical literature

Year: 2017 Venue: BMC Bioinformatics Authors: \cSenay Kafkas, Sirarat Sarntivijai, Robert Hoehndorf DOI: 10.1186/s12859-017-1978-0 Abstract Cell lines and cell types are extensively studied in biomedical research yielding to a significant amount of publications each year. Identifying cell lines and cell types precisely in publications is crucial for science reproducibility and knowledge integration. There are efforts for standardisation of the cell nomenclature based on ontology development to support FAIR principles of the cell knowledge. However, it is important to analyse the usage of cell

Using Aber-OWL for fast and scalable reasoning over BioPortal ontologies

Year: 2015 Venue: Proceedings of International Conference on Biomedical Ontologies (ICBO) Authors: Luke Slater, Georgios Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract Reasoning over biomedical ontologies using their OWL semantics has traditionally been a challenging task due to the high theoretical complexity of OWL-based automated reasoning. As a consequence, ontology repositories, as well as most other tools utilizing ontologies, either provide access to ontologies without use of automated reasoning, or limit the number of ontologies for which automated reasoning-based access is

Using AberOWL for fast and scalable reasoning over BioPortal ontologies

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Luke Slater, Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf DOI: 10.1186/s13326-016-0090-0 Abstract Reasoning over biomedical ontologies using their OWL semantics has traditionally been a challenging task due to the high theoretical complexity of OWL-based automated reasoning. As a consequence, ontology repositories, as well as most other tools utilizing ontologies, either provide access to ontologies without use of automated reasoning, or limit the number of ontologies for which automated reasoning-based access is provided

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

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

VarLand: A pipeline to map the structural landscape of missense variants at the proteome scale

Year: 2026 Venue: Journal of Biological Chemistry Authors: Francisco J. Guzman-Vega, Kelly J. Cardona-Londono, Ana C. Gonzalez-Alvarez, Karla A. Pena-Guerra, Azza Althagafi, Tanisha Khan, Robert Hoehndorf, Stefan T. Arold DOI: 10.1016/j.jbc.2025.111071 Abstract Missense variant pathogenicity often arises from disruptions to protein structural features. The integration of large-scale genetic sequencing into clinical workflows, and the availability of accurate artificial intelligence-based protein structure predictions present an opportunity to assess the structure-function relationship of

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

Year: 2018 Venue: Proceedings of the 11th International Conference Semantic Web Applications and Tools for Life Sciences, SWAT4LS 2018, Antwerp, Belgium, December 3-6, 2018. Authors: Maxat Kulmanov, Senay Kafkas, Andreas Karwath, Alexander Malic, Georgios V. Gkoutos, Michel Dumontier, Robert Hoehndorf Topics Neuro-symbolic AI · Ontology engineering Acknowledged projects ccf-microbial-cell-factories crg-bio2vec