Biomedical Informatics

INDIGENA: inductive prediction of disease–gene associations using phenotype ontologies

Year: 2026 Venue: Bioinformatics Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1093/bioinformatics/btag325 Abstract MOTIVATION: Predicting gene-disease associations (GDAs) can be framed as a ranking problem where genes are ranked for a query disease based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can

The informatics of developmental phenotypes

Year: 2025 Venue: Kaufman’s Atlas of Mouse Development Supplement Authors: Paul N. Schofield, Robert Hoehndorf, Georgios V. Gkoutos, Cynthia L. Smith DOI: 10.1016/b978-0-443-23739-3.00012-2 Topics Phenotype informatics · Biomedical informatics

Datamining with Ontologies

Year: 2016 Venue: Data Mining Techniques for the Life Sciences Authors: Robert Hoehndorf, Georgios V. Gkoutos, Paul N. Schofield DOI: 10.1007/978-1-4939-3572-7_19 Abstract The use of ontologies has increased rapidly over the past decade and they now provide a key component of most major databases in biology and biomedicine. Consequently, datamining over these databases benefits from considering the specific structure and content of ontologies, and several methods have been developed to use ontologies in datamining applications. Here, we discuss the principles of ontology structure, and

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

Ontology-Based Concept Recognition by Using Word Embeddings

Year: 2018 Venue: Bio-Ontologies COSI Authors: Sara Althubaiti, Senay Kafkas, Robert Hoehndorf Topics Neuro-symbolic AI · Biomedical informatics

Ontology based mining of pathogen-disease associations from literature

Year: 2018 Venue: Bio-Ontologies COSI Authors: Senay Kafkas, Robert Hoehndorf Topics Biomedical informatics · Applied Ontology

Enriched biodiversity data as a resource and service

Year: 2014 Venue: Biodiversity Data Journal Authors: Rutger Vos, Jordan Biserkov, Bachir Balech, Niall Beard, Matthew Blissett, Christian Brenninkmeijer, Tom van Dooren, David Eades, George Gosline, Quentin Groom, Thomas Hamann, Hannes Hettling, Robert Hoehndorf, Ayco Holleman, Peter Hovenkamp, Patricia Kelbert, David King, Don Kirkup, Youri Lammers, Thibaut DeMeulemeester, Daniel Mietchen, Jeremy Miller, Ross Mounce, Nicola Nicolson, Rod Page, Aleksandra Pawlik, Serrano Pereira, Lyubomir Penev, Kevin Richards, Guido Sautter, David Shorthouse, Marko Tahtinen, Claus Weiland, Alan Williams

Analyzing gene expression data in mice with the Neuro Behavior Ontology

Year: 2014 Venue: Mamm Genome Authors: R. Hoehndorf, J. M. Hancock, N. W. Hardy, A. M. Mallon, P. N. Schofield, G. V. Gkoutos Abstract We have applied the Neuro Behavior Ontology (NBO), an ontology for the annotation of behavioral gene functions and behavioral phenotypes, to the annotation of more than 1,000 genes in the mouse that are known to play a role in behavior. These annotations can be explored by researchers interested in genes involved in particular behaviors and used computationally to provide insights into the behavioral phenotypes resulting from differences in gene expression. We

BioHackathon 2015: Semantics of data for life sciences and reproducible research

Year: 2020 Venue: F1000Research Authors: Rutger A. Vos, Toshiaki Katayama, Hiroyuki Mishima, Shin Kawano, Shuichi Kawashima, Jin-Dong Kim, Yuki Moriya, Toshiaki Tokimatsu, Atsuko Yamaguchi, Yasunori Yamamoto, Hongyan Wu, Peter Amstutz, Erick Antezana, Nobuyuki P. Aoki, Kazuharu Arakawa, Jerven T. Bolleman, Evan Bolton, Raoul J. P. Bonnal, Hidemasa Bono, Kees Burger, Hirokazu Chiba, Kevin B. Cohen, Eric W. Deutsch, Jesualdo T. Fern\'andez-Breis, Gang Fu, Takatomo Fujisawa, Atsushi Fukushima, Alexander Garc\'\ia, Naohisa Goto, Tudor Groza, Colin Hercus, Robert Hoehndorf, Kotone Itaya, Nick Juty

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

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Year: 2021 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, William Bradlow, Dino FA. Motti, Robert Hoehndorf, Simon Ball, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2021.104216 Abstract Semantic similarity is a useful approach for comparing patient phenotypes, and holds the potential of an effective method for exploiting text-derived phenotypes for differential diagnosis, text and document classification, and outcome prediction. While approaches for context disambiguation are commonly used in text mining applications, forming a standard component of information extraction

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

Multi-faceted semantic clustering with text-derived phenotypes

Year: 2021 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, John A. Williams, Andreas Karwath, Hilary Fanning, Simon Ball, Paul N. Schofield, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2021.104904 Abstract Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering

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

Ontology based mining of pathogen--disease associations from literature

Year: 2019 Venue: Journal of Biomedical Semantics Authors: Senay Kafkas, Robert Hoehndorf Topics Biomedical informatics · Applied Ontology Acknowledged projects ccf-microbial-cell-factories crg-bio2vec

A reference quality, fully annotated diploid genome from a Saudi individual

Year: 2024 Venue: Scientific Data Authors: Maxat Kulmanov, Rund Tawfiq, Yang Liu, Hatoon Al Ali, Marwa Abdelhakim, Mohammed Alarawi, Hind Aldakhil, Dana Alhattab, Ebtehal A. Alsolme, Azza Althagafi, Angel Angelov, Salim Bougouffa, Patrick Driguez, Changsook Park, Alexander Putra, Ana M. Reyes-Ramos, Charlotte A. E. Hauser, Ming Sin Cheung, Malak S. Abedalthagafi, Robert Hoehndorf DOI: 10.1038/s41597-024-04121-2 Topics Biomedical informatics

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

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

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

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

Genomic landscape in Saudi patients with hepatocellular carcinoma using whole-genome sequencing: a pilot study

Year: 2023 Venue: Frontiers in Gastroenterology Authors: Mazen Hassanain, Yang Liu, Weam Hussain, Albandri Binowayn, Duna Barakeh, Ebtehal Alsolme, Faisal AlSaif, Ghaida Almasaad, Mohammed AlSwayyed, Maram Alaqel, Rana Aljunidel, Sherin Abdelrahman, Charlotte A. E. Hauser, Saleh Alqahtani, Robert Hoehndorf, Malak Abedalthagafi DOI: 10.3389/fgstr.2023.1205415 Abstract Our findings indicate that most of the HCC patients possess cancer-related genetic variants, and the altered pathways in these patients exhibit similarities. Notably, resistant patients exhibit a higher frequency of aberrations in

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

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

Age-related differences in gene expression and pathway activation following heatstroke

Year: 2025 Venue: Physiological Genomics Authors: Maria Gomez, Saeed Al Mahri, Mashan Abdullah, Shuja Shafi Malik, Saber Yezli, Yara Yassin, Anas Khan, Cynthia Lehe, Sameer Mohammad, Robert Hoehndorf, Abderrezak Bouchama DOI: 10.1152/physiolgenomics.00053.2024 Abstract This study investigates the molecular responses to heatstroke in young and old patients by comparing whole-genome transcriptomes between age groups. We analyzed transcriptomic profiles from patients categorized into two age-defined cohorts: young (mean age = 44.9 ± 6 yr) and old (mean age = 66.1 ± 4 yr). Control subjects

Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics

Year: 2015 Venue: Nature Genetics Authors: Martin Hrab\ve de Angelis, George Nicholson, Mohammed Selloum, Jacqueline K White, Hugh Morgan, Ramiro Ramirez-Solis, Tania Sorg, Sara Wells, Helmut Fuchs, Martin Fray, David J Adams, Niels C Adams, Thure Adler, Antonio Aguilar-Pimentel, Dalila Ali-Hadji, Gregory Amann, Philippe Andr\'e, Sarah Atkins, Aurelie Auburtin, Abdel Ayadi, Julien Becker, Lore Becker, Elodie Bedu, Raffi Bekeredjian, Marie-Christine Birling, Andrew Blake, Joanna Bottomley, Michael R Bowl, V\'eronique Brault, Dirk H Busch, James N Bussell, Julia Calzada-Wack, Heather Cater

Ontology based text mining of gene-phenotype associations: application to candidate gene prediction

Year: 2019 Venue: Database Authors: Senay Kafkas, Robert Hoehndorf Abstract Gene–phenotype associations play an important role in understanding the disease mechanisms which is a requirement for treatment development. A portion of gene–phenotype associations are observed mainly experimentally and made publicly available through several standard resources such as MGI. However, there is still a vast amount of gene–phenotype associations buried in the biomedical literature. Given the large amount of literature data, we need automated text mining tools to alleviate the burden in manual curation of

Hyaline Arteriolosclerosis in 30 Strains of Aged Inbred Mice

Year: 2019 Venue: Veterinary Pathology Authors: Timothy K. Cooper, Kathleen A. Silva, Victoria E. Kennedy, Sarah M. Alghamdi, Robert Hoehndorf, Beth A. Sundberg, Paul N. Schofield, John P. Sundberg DOI: 10.1177/0300985819844822 Abstract During a screen for vascular phenotypes in aged laboratory mice, a unique discrete phenotype of hyaline arteriolosclerosis of the intertubular arteries and arterioles of the testes was identified in several inbred strains. Lesions were limited to the testes and did not occur as part of any renal, systemic, or pulmonary arteriopathy or vasculitis phenotype

Predicting candidate genes from phenotypes, functions and anatomical site of expression

Year: 2021 Venue: Bioinformatics Authors: Jun Chen, Azza Althagafi, Robert Hoehndorf DOI: 10.1093/bioinformatics/btaa879 Abstract Supplementary data are available at Bioinformatics online. Topics Rare disease · Biomedical informatics

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

Ontology-based prediction of cancer driver genes

Year: 2019 Venue: Scientific Reports Authors: Sara Althubaiti, Andreas Karwath, Ashraf Dallol, Adeeb Noor, Shadi Salem Alkhayyat, Rolina Alwassia, Katsuhiko Mineta, Takashi Gojobori, Andrew D Beggs, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity

Genomic diversity and antimicrobial resistance of Staphylococcus aureus in Saudi Arabia: a nationwide study using whole-genome sequencing

Year: 2025 Venue: Microbial Genomics Authors: Mohammed S. Alarawi, Musaad Altammami, Mohammed Abutarboush, Maxat Kulmanov, Dalal M. Alkuraithy, Senay Kafkas, Robert Radley, Marwa Abdelhakim, Hind Aldakhil, Reema A. Bawazeer, Mohammed A. Alolayan, Basel M. Alnafjan, Abdulaziz A. Huraysi, Amani Almaabadi, Bandar A. Suliman, Areej G. Aljohani, Hassan A. Hemeg, Mohammed S. Almogbel, Meshari Alazmi, Abdulrahman S. Bazaid, Turki S. Abujamel, Anwar M. Hashem, Ibrahim A. Al-Zahrani, Mohammed S. Abdoh, Haya I. Hobani, Rakan F. Felemban, Wafaa A. Alhazmi, Pei-Ying Hong, Majed F. Alghoribi, Sameera

Aber-OWL: a framework for ontology-based data access in biology

Year: 2015 Venue: BMC Bioinformatics Authors: Robert Hoehndorf, Luke Slater, Paul N Schofield, Georgios V Gkoutos Abstract Background: Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning. Results: We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web

BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services

Year: 2019 Venue: F1000Research Authors: T Katayama, S Kawashima, G Micklem, S Kawano, JD Kim, S Kocbek, S Okamoto, Y Wang, H Wu, A Yamaguchi, Y Yamamoto, E Antezana, KF Aoki-Kinoshita, K Arakawa, M Banno, J Baran, JT Bolleman, RJP Bonnal, H Bono, JT Fernandez-Breis, R Buels, MP Campbell, H Chiba, PJA Cock, KB Cohen, M Dumontier, T Fujisawa, T Fujiwara, L Garcia, P Gaudet, E Hattori, R Hoehndorf, K Itaya, M Ito, D Jamieson, S Jupp, N Juty, A Kalderimis, F Kato, H Kawaji, T Kawashima, AR Kinjo, Y Komiyama, M Kotera, T Kushida, J Malone, M Matsubara, S Mizuno, S Mizutani, H Mori, Y Moriya, K

DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes

Year: 2021 Venue: Bioinformatics Authors: Wang Liu-Wei, Senay Kafkas, Jun Chen, Nicholas J Dimonaco, Jesper Tegner, Robert Hoehndorf DOI: 10.1093/bioinformatics/btab147 Abstract Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms
Schematic of machine learning for survival analysis: a neural network feeding stratified descending survival curves

Mohammad Ashhad successfully defends MSc thesis on machine learning for survival analysis

1 min read ·
Congratulations to Mohammad Ashhad, who successfully defended his Master’s thesis “ Machine Learning Methods for Survival Analysis: From Outcome-Conditioned Data Synthesis to Decoupled Ranking and Calibration” on 3 May 2026 in KAUST’s Bioengineering Program. Mohammad will now continue in the group as a PhD student in the Computer Science Program. The thesis tackles two persistent obstacles to building accurate, generalizable survival models from biomedical data: the scarcity of shareable training data, and the trade-off between expressiveness and robustness that classical and deep-learning

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, standardize 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

M.S. (former), Bioengineering
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

M.S. (former), Bioscience
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

M.S. (former), Bioengineering
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

M.S. (former), Computer Science
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

M.S. (former), Bioengineering
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 (former), 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