Semantic similarity

Semantic similarity

Semantic similarity measures over biomedical ontologies sit at the core of several of our research lines, from disease-gene prioritization to ontology-aware function transfer and biodiversity knowledge-graph search. We develop, benchmark and apply measures that exploit the OWL axiomatic structure of an ontology rather than only its lexical or taxonomic skeleton, and we have repeatedly shown that this richer semantics translates into measurable improvements on downstream prediction tasks. Operating within KAUST's Computer Science Program, our distinctive contribution is that we treat similarity

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

Bio2Vec: Smart analytics infrastructure for the life sciences

By the mid-2010s the life sciences had produced an extraordinary investment in machine-readable knowledge: biomedical ontologies were used throughout biology to annotate data, and large RDF knowledge graphs such as Bio2RDF aggregated billions of statements from dozens of major databases. At the same time, large personal genomic datasets, the UK 100,000 Genomes project, UK Biobank, and the Saudi Human Genome Program, were coming online, and translating these into clinical insight depended on integrating them with that existing background knowledge. Generic knowledge-graph machine learning

CompleX: Variant Prioritization in Complex Disease

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
crg explainable ml ontologies

Towards sound, complete, and explainable machine learning with biomedical ontologies (CRG11)

Deep learning has driven much of the recent progress in bioinformatics, but the resulting models are essentially black boxes: it is hard or impossible to ask why a prediction was made, to verify it against existing knowledge, or to detect biases that emerge from the interaction of dataset, architecture, and training objective. In clinical and biomedical settings these limitations matter, both because clinicians need to trust the systems they use and because formal guarantees of soundness and fairness can only be obtained when the inferential machinery can be inspected. The grand challenge