Biomedical Informatics

Biomedical informatics

Our biomedical informatics work converts heterogeneous research-grade data into usable inputs for clinicians and computational biologists. Within KAUST's Computer Science Program, we build biomedical knowledge bases, mine text for structured biological assertions, standardise clinical phenotype encodings, and develop analytics over electronic health records and rare-disease cohorts. The distinctive feature of our approach is that almost every component is grounded in formal ontologies, so that text-mined facts, curated databases and clinical observations share a common semantic substrate and

NanoDesigner

Iterative refinement framework for nanobody/CDR design that explicitly models the antigen–CDR interdependence; companion code to the NanoDesigner paper. Get it GitHub: https://github.com/bio-ontology-research-group/NanoDesigner ★ 16 Developed in projects Disease Models from Patient-derived Leukemic Cells in Biomimetic Peptide Scaffolds for Precision Medicine Applications Category: Knowledge Graphs & Drug Discovery

PathoPhenoDB

Curated database of pathogens and the disease phenotypes they cause, distributed as an OWL ontology and an interactive web application. Get it GitHub: https://github.com/bio-ontology-research-group/pathophenodb ★ 8 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Knowledge Graphs & Drug Discovery

DeepMOCCA

Graph neural network for cancer survival analysis that integrates multi-omics (mutation, expression, methylation, CNV) with a curated cancer knowledge graph. Get it GitHub: https://github.com/bio-ontology-research-group/DeepMOCCA ★ 14 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Knowledge Graphs & Drug Discovery

Multi-Drug Embedding

Drug repurposing method that learns joint embeddings of drugs, targets and diseases from biomedical knowledge graphs and the scientific literature. Get it GitHub: https://github.com/bio-ontology-research-group/multi-drug-embedding ★ 36 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Knowledge Graphs & Drug Discovery

SmuDGE

Semantic disease-gene embeddings; integrates phenotype, function and pathway ontologies into a unified vector space for downstream prediction. Get it GitHub: https://github.com/bio-ontology-research-group/SMUDGE ★ 12 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences CompleX: Variant Prioritization in Complex Disease Category: Knowledge Graphs & Drug Discovery

Sawsan Al Boeisa

Sawsan Al Boeisa completed her MSc in Bioengineering at KAUST in 2025 under the supervision of Robert Hoehndorf, with co-supervision and laboratory hosting from the Merzaban lab. She defended her thesis on 17 November 2025 and the final document was archived in the KAUST repository in December 2025. Her thesis, Differential Effects of p38 MAPK Inhibition on Chemoresistance in Patients with Colorectal Cancer, is a wet-lab pilot study in translational cancer biology. It investigates whether pharmacological inhibition of p38 MAPK can reverse or modulate chemoresistance in patient-derived

Melissa Rios Zertuche

Melissa Maria Rios Zertuche completed her MSc in Bioscience at KAUST in 2024 under the supervision of Robert Hoehndorf. Her thesis project was carried out in collaboration with the Eppinger lab and developed a computational pipeline for the rational design and optimisation of nanobodies. Her thesis, Establishment and Evaluation of a Computational Workflow for the Design and Optimization of Nanobodies, addresses the lack of comprehensive end-to-end computational tools tailored to the design of single-domain antibodies. Nanobodies (VHHs) are roughly one-tenth the size of conventional antibodies

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

Md Nurul Muttakin

Md Nurul Muttakin completed his MSc in Computer Science at KAUST in 2023 under the supervision of Robert Hoehndorf. His research focused on machine learning methods for predicting protein function from three-dimensional structural information. His thesis, 3D conformation-based protein function prediction, investigated how structural information should be exploited by deep learning models in light of the availability of near-experimental-accuracy structures from AlphaFold. Almost all existing structure-aware protein function predictors combine graph neural networks (GNNs) operating on 2D

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

Application of AI in Bioinformatics (2025)

Year: 2025 Code: CS 321 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Neuro-symbolic AI, Protein function

Algorithms in Bioinformatics (2020)

Year: 2020 Code: CS 290 Program: Computer Science / Bioengineering Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Data Analytics (2021)

Year: 2021 Code: CS 220 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Neuro-symbolic AI

Algorithms in Bioinformatics (2022)

Year: 2022 Code: CS 249 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Data Analytics (2023)

Year: 2023 Code: CS 220 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Neuro-symbolic AI

Algorithms in Bioinformatics (2025)

Year: 2025 Code: CS 249 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and