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 Computational Bioscience Research Center's Microbial Cell Factories initiative, led by Vladimir Bajic with Hoehndorf as CoI heading the data-integration

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

Improving health of Saudi population

Mon, May 18 2026

Neuro-Symbolic AI Rare disease

Translating modern biomedical knowledge into healthcare for Saudi Arabia requires methods that work on local data: a population with high consanguinity, a distinctive spectrum of inborn errors of metabolism, and clinical phenotypes that are not always well represented in international reference resources. The project, led by Hoehndorf at the Computational Bioscience Research Center, developed health-informatics methods and resources oriented towards Saudi-population data, with a focus on rare disease, drug treatment, and the formal representation of phenotype information. The publications

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

Development of Algorithms for Biotechnology and Biomedical Applications

Mon, May 18 2026

Neuro-Symbolic AI

This CBRC Competitive Funding project (2021–2023) functioned as an umbrella programme for algorithm development across the group's biotechnology and biomedical work, with a deliberate focus on metabolic modelling — predicting metabolic function from genome, predicting interactions from structure, and learning from interaction networks to identify disease-relevant biology. The unifying scientific bet was that systems biology requires algorithms that operate not on isolated molecules but on the networks of interactions within and between organisms, and that machine learning over those networks

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

Enabling mangrove restoration by AI-tailored microbiome fortification

Mon, May 18 2026

genomics Microbial communities Neuro-Symbolic AI

This short collaboration (2023, funded under the KAUST Center for Living Innovation programme) developed AI-tailored microbiome work in support of coastal mangrove restoration in Saudi Arabia. The project ran as a sister activity to the BORG group's larger desert-microbiome programme with the NTGC, applying analogous metagenomic and functional-annotation pipelines to mangrove sediment and root-associated communities rather than to arid-soil systems. Heribert Hirt (DARWIN21, KAUST) was the Principal Investigator, with Hoehndorf as Co-Investigator providing the computational microbiome analysis

Enabling desert revegetation by AI-tailored soil microbiome fortification

Mon, May 18 2026

Microbial communities Neuro-Symbolic AI protein function

Roughly twelve million hectares of dryland are lost every year worldwide, and the megaprojects launched to reverse desertification — the Sahel's Great Green Wall is the canonical example — typically fail because most planted trees die when irrigation stops. The biological reason is that desert soils are low in nutrients and high in salinity, and the microbial communities that mediate nitrogen, phosphate, and mineral acquisition for plants are missing or unbalanced. This project, led by Heribert Hirt's plant-microbiome group with our group and the Modelling and Simulation group of Gabriel

Disease Models from Patient-derived Leukemic Cells in Biomimetic Peptide Scaffolds for Precision Medicine Applications

Sun, May 17 2026

Neuro-Symbolic AI Rare disease

Acute myeloid leukemia (AML) has a five-year survival rate of roughly 24% in Saudi Arabia and is the second most common adult leukemia subtype in the Kingdom. Treatment selection still rests on cytogenetic risk stratification — a coarse instrument that misses much of the molecular heterogeneity now known to drive drug response. Compounding the problem, almost every published AML drug-response prediction model has been trained on Western cohorts, and existing 3D culture systems for leukemia rely on animal-derived matrices that resist standardisation. This project (2023–2026, KAUST Smart-Health

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

Sun, May 17 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

KAUST Center of Excellence for Generative AI (Health and Wellness, BCB theme)

Sun, May 17 2026

Drug mechanisms Neuro-Symbolic AI Rare disease

The KAUST Center of Excellence for Generative AI has a Health and Wellness theme, and within that theme the Bioinformatics and Computational Biology (BCB) workstream is responsible for the bio-side problems that generic generative models cannot solve on their own: protein and antibody design, population-specific drug development, and clinical foundation models that can actually reason with structured biomedical knowledge. The BORG group leads the neuro-symbolic and ontology-based components of this work (Sub-WBS FCC/1/5940-07-02), in partnership with Xin Gao's group on AI/drug design and the

Personalized cancer treatment prediction (KCSH Pathway to Impact 2025)

Sun, May 17 2026

Neuro-Symbolic AI Rare disease

Predicting which drug will work for which patient is one of the oldest unsolved problems in oncology, and it is especially acute in Saudi Arabia, where colorectal cancer is the most common cancer in men and is shifting toward younger ages, and hepatocellular carcinoma incidence has roughly doubled in two decades. Contemporary machine learning models for drug response largely ignore the decades of curated biological knowledge that surrounds them, and they are almost never trained on Saudi populations. This project, funded under the KCSH Pathway to Impact 2025 call and running 2025-2026, aims to

KAUST Smart Health Initiative 2024 round (SHI2024)

Sun, May 17 2026

Neuro-Symbolic AI

This is the active sub-funding line under the KAUST Smart Health Initiative 2024 round (activated grant REI/1/6368-01-01, with associated sub-WBSes including FCC/1/5932-10-02). The Smart Health Initiative is one of KAUST's strategic research initiatives, and the 2024 round provides flexible seed funding for projects that translate the group's work on knowledge-based machine learning, multi-omics integration, and ontology-driven disease modelling into concrete clinical and population-health applications relevant to Saudi Arabia. At the time of writing, the funded sub-projects under this line

A public Saudi pangenome as reference for genomics in the Middle East

Sun, May 17 2026

genomics Neuro-Symbolic AI Rare disease

Human reference genomes such as GRCh38 are built largely from a single individual of mixed African-European ancestry, and they systematically misrepresent the genetic structure of populations that diverged from that backbone, especially populations like those of the Arabian Peninsula that were historically organised in tribal structures with high in-tribe marriage. The consequence in clinical practice is a reference bias that inflates the apparent variant count by millions, complicates interpretation, and lengthens diagnostic turnaround. With more than half of marriages in Saudi Arabia

Robert Hoehndorf

Associate Professor, Computer Science

artificial intelligence bioinformatics biomedical data knowledge representation Neuro-Symbolic AI neuro-symbolic methods

Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Olga Mashkova

Ph.D. Student, Computer Science

machine learning bioinformatics knowledge representation Deep learning Neuro-Symbolic AI

PhD student in Computer Science. Research interests: machine learning, bioinformatics, mathematics, knowledge representation and reasoning, deep learning, algorithms.

Fernando Patricio Zhapa-Camacho

Postdoctoral Research Fellow, Computer Science

Knowledge representation and reasoning Neuro-Symbolic AI bioinformatics

Fernando Patricio Zhapa Camacho is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST, Fernando obtained a bachelor's degree in Information Technology from Yachay Tech University, Ecuador. Research Interests Fernando’s research interests include knowledge representation and reasoning, machine learning and bioinformatics. He is interested in artificial intelligence and logic integration with applications in biology. Professional Profile: • 2020 to present: Graduate Student, KAUST, Thuwal, Saudi Arabia. •