Topics

Research topics the Bio-Ontology Research Group works on.

Neuro-symbolic AI

I 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. I 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.

Keywords: ontology embeddings description logic geometric embeddings EL++ ALC neuro-symbolic AI knowledge graph embedding mOWL

Ontology engineering

I 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.

Keywords: AberOWL Semantic Web OWL OWL EL reasoning ontology-based data access linked data interoperability

Applied Ontology

I use ontologies to standardize and analyze complex phenotypes across domains. My 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.

Keywords: formal ontology GFO GFO-Bio ontology of functions phenotype ontology FLOPO ontology design patterns

Protein function

Large-scale ontologies like the Gene Ontology (GO) provide essential background knowledge for understanding protein activity. I work on 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.

Keywords: Gene Ontology GO DeepGO DeepGOPlus DeepGO-SE DeepGOZero protein function semantic entailment metagenomics function

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). I develop systems like PhenomeNET and PVP that use automated reasoning and machine learning to prioritize disease-causing genomic variants based on their phenotypic consequences.

Keywords: PhenomeNET DeepPVP PVP variant prioritization Human Phenotype Ontology HPO rare disease complex disease consanguinity Saudi population

Drug mechanisms

I 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.

Keywords: drug-target interaction DTI-Voodoo drug repurposing PharmGKB SBML Harvester causal knowledge graph adverse drug reaction systems biology

Genomics

I 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.

Keywords: Saudi pangenome reference genome pangenome graph variant calling structural variant whole-genome sequencing GWAS population genomics

Biomedical informatics

I 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.

Keywords: biomedical knowledge base PathoPhenoDB PhenomeBrowser text mining biomedical NLP clinical informatics EHR analytics phenotype standardisation

Semantic similarity

I 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.

Keywords: semantic similarity ontology similarity Resnik Lin phenotype similarity PhenomeNET similarity OPA2Vec

Microbial communities

I 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 characterisation of patient and environmental microbiomes.

Keywords: metagenomics microbial communities DeepGOMeta soil microbiome marine microbiome extremophile functional metagenomics

Phenotype informatics

I 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 standardise phenotype descriptions, and computational pipelines that link phenotype data to underlying genes, variants, and diseases.

Keywords: phenotype ontology HPO MP FLOPO phenotype standardisation trait recognition PhenomeNET cross-species phenotypes

Bioengineering

I contribute computational and omics analysis to collaborative bioengineering projects. Examples include analysing 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.

Keywords: bioengineering biomimetic scaffold patient-derived disease model multi-omics tissue engineering transcriptomics metabolomics