Rare disease

Safana Bakheet

Safana Bakheet completed her MSc in Bioengineering at KAUST in 2025 under the supervision of Robert Hoehndorf. Her work was carried out in collaboration with Fernando Zhapa-Camacho and developed methods for prioritising disease-causing genes by combining ontology-based phenotype representations with supervised knowledge graph embeddings. Her thesis, An Inductive, Supervised Gene-Disease Associations Method, introduces a ranking model that scores candidate disease-causing genes based on the similarity of their associated phenotypes, represented in the Mammalian Phenotype Ontology (MP) and the

Amal Alhelal

Amal Alhelal completed her MSc in Bioengineering at KAUST in 2024 under the supervision of Robert Hoehndorf. She was a student in the Bioinformatics and Machine Learning track of the Bioengineering programme, based at the Computational Bioscience Research Center (CBRC), and defended her thesis in May 2024. Her thesis, titled Protein functional domain identification methodology (defended under the working title Exploring the association between protein functional domains and pathogenicity information derived from AI tools), investigated computational approaches to delineate functional domains

Improvement of genetic variant prioritization technology

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

Whole genome sequencing of rare disease patients

This 2019 Office of Sponsored Research pilot was a short, focused collaboration with KAUST Health to put whole-genome sequencing in front of rare-disease cases for the first time at KAUST. Saudi Arabia has an unusually high prevalence of recessive Mendelian disease driven by high rates of consanguinity, yet at the time first-tier genomic diagnosis was rarely available locally. The pilot was designed to test the end-to-end pathway — patient identification, sample handling, sequencing, variant calling, phenotype-driven prioritization, and clinical interpretation — inside KAUST rather than

Improving health of Saudi population

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

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

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
smart health peptide leukemia

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

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
coe genai bcb

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

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
kcsh pathway to impact

Personalized cancer treatment prediction (KCSH Pathway to Impact 2025)

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
smart health saudi pangenome

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

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