Rare disease

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

Biomedical Informatics Neuro-Symbolic AI Rare disease

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

Phenotype-driven discovery of digenic variants in personal genome sequences

Rare disease genomics Phenotype informatics

Year: 2017 Venue: Proceedings of VarI-SIG Authors: Imane Boudellioua, Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Identification of variants associated with inherited diseases is a major challenge, in particular in the analysis of clinical sequence data from individual patients. An increasing number of Mendelian diseases have been identified in which two or more variants in multiple genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. Information

Integrating phenotype ontologies with PhenomeNET

Rare disease Phenotype informatics

Year: 2016 Venue: Proceedings of Ontology Matching Workshop 2016 Authors: Miguel Rodriguez-Garcia, Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract PhenomeNET is a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Here, we apply the PhenomeNET to identify related classes from four phenotype and

EMC10 homozygous variant identified in a family with global developmental delay, mild intellectual disability, and speech delay

Rare disease genomics

Year: 2020 Venue: Clinical Genetics Authors: Muhammad Umair, Mariam Ballow, Abdulaziz Asiri, Yusra Alyafee, Abeer Tuwaijri, Kheloud M. Alhamoudi, Taghrid Aloraini, Marwa Abdelhakim, Azza Thamer Althagafi, Senay Kafkas, Lamia Alsubaie, Muhammad Talal Alrifai, Robert Hoehndorf, Ahmed Alfares, Majid Alfadhel DOI: 10.1111/cge.13842 Abstract In recent years, several genes have been implicated in the variable disease presentation of global developmental delay (GDD) and intellectual disability (ID). The endoplasmic reticulum membrane protein complex (EMC) family is known to be involved in GDD and ID

Causal relationships between diseases mined from the literature improve the use of polygenic risk scores

Biomedical Informatics Rare disease

Year: 2024 Venue: Bioinformatics Authors: Sumyyah Toonsi, Iris Ivy Gauran, Hernando Ombao, Paul N Schofield, Robert Hoehndorf DOI: 10.1093/bioinformatics/btae639 Abstract The data are available through https://github.com/bio-ontology-research-group/causal-relations-between-diseases. Topics Biomedical informatics · Rare disease

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project

Rare disease genomics

Year: 2024 Venue: Human Genomics Authors: Sarah L. Stenton, Melanie C. O’Leary, Gabrielle Lemire, Grace E. VanNoy, Stephanie DiTroia, Vijay S. Ganesh, Emily Groopman, Emily O’Heir, Brian Mangilog, Ikeoluwa Osei-Owusu, Lynn S. Pais, Jillian Serrano, Moriel Singer-Berk, Ben Weisburd, Michael W. Wilson, Christina Austin-Tse, Marwa Abdelhakim, Azza Althagafi, Giulia Babbi, Riccardo Bellazzi, Samuele Bovo, Maria Giulia Carta, Rita Casadio, Pieter-Jan Coenen, Federica De Paoli, Matteo Floris, Manavalan Gajapathy, Robert Hoehndorf, Julius O. B. Jacobsen, Thomas Joseph, Akash Kamandula, Panagiotis

Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

Neuro-Symbolic AI Rare disease

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 prioritization of novel causative genomic variants

Rare disease Semantic similarity

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

PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

Biomedical Informatics Rare disease

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

OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

Rare disease genomics

Year: 2018 Venue: Scientific Reports Authors: Imane Boudellioua, Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Purpose: An increasing number of Mendelian disorders have been identified for which two or more variants in one or more genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of variants underlying oligogenic diseases in individual whole exome or whole

Genomic landscape of retinoblastoma: Insights into risk stratification and precision pediatric Neuro-Oncology

genomics Rare disease

Year: 2025 Venue: Neuro-Oncology Pediatrics Authors: Azza Maktabi, Yang Liu, Saleh Almesfer, Marwa Abdelhakim, Hind Aldakhil, Maxat Kulmanov, Deepak P Edward, Robert Hoehndorf, Malak Abedalthagafi DOI: 10.1093/neuped/wuaf017 Abstract Abstract Background Retinoblastoma is the most common intraocular malignancy of childhood, yet its genomic landscape remains incompletely defined, particularly in understudied populations. Beyond RB1 loss, the contribution of additional somatic and germline alterations to disease heterogeneity and clinical behavior is unclear. Methods We performed whole-exome

The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients

Rare disease Biomedical Informatics

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

Rare disease Biomedical Informatics

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

Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases

Rare disease Semantic similarity

Year: 2015 Venue: Scientific Reports Authors: Robert Hoehndorf, Paul N Schofield, Georgios V Gkoutos Abstract Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic

Similarity-based search of model organism, disease and drug effect phenotypes

Semantic similarity Rare disease Drug mechanisms

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

DeepPVP: phenotype-based prioritization of causative variants using deep learning

Rare disease Neuro-Symbolic AI

Year: 2019 Venue: BMC Bioinformatics Authors: Imane Boudellioua, Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient{

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

Biomedical Informatics Rare disease

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

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

Rare disease Biomedical Informatics

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

CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs)

Rare disease Phenotype informatics

Year: 2025 Venue: Human Genetics Authors: Maria Cristina Aspromonte, Alessio Del Conte, Shaowen Zhu, Wuwei Tan, Yang Shen, Yexian Zhang, Qi Li, Maggie Haitian Wang, Giulia Babbi, Samuele Bovo, Pier Luigi Martelli, Rita Casadio, Azza Althagafi, Sumyyah Toonsi, Maxat Kulmanov, Robert Hoehndorf, Panagiotis Katsonis, Amanda Williams, Olivier Lichtarge, Su Xian, Wesley Surento, Vikas Pejaver, Sean D. Mooney, Uma Sunderam, Rajgopal Srinivasan, Alessandra Murgia, Damiano Piovesan, Silvio C. E. Tosatto, Emanuela Leonardi DOI: 10.1007/s00439-024-02722-w Abstract Abstract The Genetics of

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

Rare disease Neuro-Symbolic AI

Year: 2024 Venue: Bioinformatics Authors: Azza Althagafi, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1093/bioinformatics/btae301 Abstract EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP. Topics Rare disease · Neuro-symbolic AI

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning

Rare disease genomics

Year: 2022 Venue: Bioinformatics Authors: Azza Althagafi, Lamia Alsubaie, Nagarajan Kathiresan, Katsuhiko Mineta, Taghrid Aloraini, Fuad Al Mutairi, Majid Alfadhel, Takashi Gojobori, Ahmad Alfares, Robert Hoehndorf DOI: 10.1093/bioinformatics/btab859 Abstract Supplementary data are available at Bioinformatics online. Topics Rare disease · Genomics

Contribution of model organism phenotypes to the computational identification of human disease genes

Rare disease Phenotype informatics Semantic similarity

Year: 2022 Venue: Disease Models & Mechanisms Authors: Sarah Alghamdi, Paul N. Schofield, Robert Hoehndorf DOI: 10.1242/dmm.049441 Abstract Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype-phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of

What is the right sequencing approach? Solo VS extended family analysis in consanguineous populations

genomics Rare disease

Year: 2020 Venue: BMC Medical Genomics Authors: Ahmed Alfares, Lamia Alsubaie, Taghrid Aloraini, Aljoharah Alaskar, Azza Althagafi, Ahmed Alahmad, Mamoon Rashid, Abdulrahman Alswaid, Ali Alothaim, Wafaa Eyaid, Faroug Ababneh, Mohammed Albalwi, Raniah Alotaibi, Mashael Almutairi, Nouf Altharawi, Alhanouf Alsamer, Marwa Abdelhakim, Senay Kafkas, Katsuhiko Mineta, Nicole Cheung, Abdallah M. Abdallah, Stine B\"uchmann-M\oller, Yoshinori Fukasawa, Xiang Zhao, Issaac Rajan, Robert Hoehndorf, Fuad Al Mutairi, Takashi Gojobori, Majid Alfadhel DOI: 10.1186/s12920-020-00743-8 Abstract There was no

DDIEM: drug database for inborn errors of metabolism

Drug mechanisms Rare disease

Year: 2020 Venue: Orphanet Journal of Rare Diseases Authors: Marwa Abdelhakim, Eunice McMurray, Ali Raza Syed, Senay Kafkas, Allan Anthony Kamau, Paul N Schofield, Robert Hoehndorf DOI: 10.1186/s13023-020-01428-2 Abstract Abstract Background Inborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

Rare disease Phenotype informatics Neuro-Symbolic AI

Year: 2020 Venue: PLOS Computational Biology Authors: Maxat Kulmanov, Robert Hoehndorf DOI: 10.1371/journal.pcbi.1008453 Abstract Author summary Gene–phenotype associations can help to understand the underlying mechanisms of many genetic diseases. However, experimental identification, often involving animal models, is time consuming and expensive. Computational methods that predict gene–phenotype associations can be used instead. We developed DeepPheno, a novel approach for predicting the phenotypes resulting from a loss of function of a single gene. We use gene functions and gene expression

Improvement of genetic variant prioritization technology

Mon, Jan 1 2018 - Tue, Dec 31 2019

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

Whole genome sequencing of rare disease patients

Tue, Jan 1 - Tue, Dec 31 2019

Rare disease

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

Tue, Jan 1 2019 - Fri, Dec 31 2021

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 KAUST's Computational Bioscience Research Center (CBRC, now dissolved), 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

CompleX: Variant Prioritization in Complex Disease

Tue, Jan 1 2019 - Fri, Dec 31 2021

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

IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information

Fri, Jan 1 2021 - Sun, Dec 31 2023

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

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

Sun, Jan 1 2023 - Thu, Dec 31 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 standardization. This project (2023–2026, KAUST Smart-Health

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

Mon, Jan 1 2024

Research

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)

Wed, Jan 1 2025 - Thu, Dec 31 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

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

Mon, Jan 1 2024 - Thu, Dec 31 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 organized 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