Topics
Research topics the Bio-Ontology Research Group works on. Open a topic for the full overview, related projects, software, and publications.
Neuro-symbolic AI
We work on methods that integrate symbolic knowledge with statistical learning. This includes mapping entities in formal ontologies into vector spaces while preserving their semantic relations. We develop embedding frameworks for Description Logics (e.g., EL++ and ALC) that provide mathematical guarantees for logical soundness and approximate the interpretation of formalized theories.
Ontology engineering
We develop architectures for processing massive, heterogeneous data using Semantic Web standards. This work includes the AberOWL infrastructure for ontology-based data access and methods for establishing interoperability across distributed databases through linked knowledge graphs.
Applied Ontology
We use ontologies to standardize and analyze complex phenotypes across domains. Earlier work focused on foundational ontologies, including the General Formal Ontology (GFO) and its biological extension GFO-Bio, as well as the development of a formal ontology of functions to curate functional knowledge in the life sciences.
Protein function
Large-scale ontologies like the Gene Ontology (GO) provide essential background knowledge for understanding protein activity. We develop the DeepGO family of systems, which utilize formalized axioms to constrain deep learning models for protein function prediction. These systems are used to derive functional insights from sequence and interaction data.
Rare disease
The diagnosis of rare diseases requires the integration of patient-specific data with large-scale background knowledge, such as the Human Phenotype Ontology (HPO). We develop systems like PhenomeNET and PVP that use automated reasoning and machine learning to prioritize disease-causing genomic variants based on their phenotypic consequences.
Drug mechanisms
We apply ontologies and knowledge graphs to model drug-target interactions, drug indications and adverse drug reactions. This work links molecular data to systems biology through causal knowledge graphs, enabling the identification of mechanistic relationships and potential drug repurposing targets.
Genomics
We contribute to the development of genomic resources and the analysis of population-specific genomic data. This includes reference genome assemblies, pangenome graphs for the Saudi and wider Middle Eastern population, variant-calling and structural-variant pipelines and the analysis of antimicrobial resistance from whole-genome sequencing.
Biomedical informatics
We work on biomedical informatics infrastructure that turns research-grade data into usable inputs for clinicians and researchers. This includes biomedical knowledge-base construction (PathoPhenoDB, PhenomeNET, PhenomeBrowser), text-mining of biomedical literature, integration of clinical phenotype encodings and analytics over electronic health records.
Semantic similarity
We develop and benchmark semantic similarity measures over biomedical ontologies, including measures that operate on the OWL axiomatic structure of an ontology rather than only on its lexical or taxonomic skeleton. These measures underpin phenotype-based disease gene prioritization, ontology-aware protein function transfer and biodiversity knowledge graph search.
Microbial communities
We develop methods that lift single-protein function prediction up to the level of microbial communities, combining ontology-aware deep learning with multi-scale systems approaches. Applications include desert-soil and mangrove-microbiome design, bioprospecting from Saudi-Arabian extremophile habitats and metagenomics-driven functional characterization of patient and environmental microbiomes.
Phenotype informatics
We develop the informatics infrastructure for phenotype data across species and clinical settings: phenotype ontologies (HPO, MP, ZP, FLOPO, plant traits), cross-species phenotype crosswalks, tools that capture and standardize phenotype descriptions and computational pipelines that link phenotype data to underlying genes, variants and diseases.
Bioengineering
We contribute computational and omics analysis to collaborative bioengineering projects. Examples include analyzing transcriptomic and metabolomic responses of cells cultured in biomimetic peptide scaffolds, patient-derived disease-model analysis for precision medicine and the integration of multi-omics data with engineered biological systems.