Semantic similarity

Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology

Year: 2011 Venue: Proceedings of the 3rd Workshop for Ontologies in Biomedicine and Life sciences (OBML) Authors: Anika Oellrich, Robert Hoehndorf, Georgios V. Gkoutos, Dietrich Rebholz-Schuhmann Abstract Researchers use animal studies to better understand human diseases. In recent years, large-scale phenotype studies such as Phenoscape and EuroPhenome have been initiated to identify genetic causes of a species phenome. Species-specific phenotype ontologies are required to capture and report about all findings and to automatically infer results relevant to human diseases. The integration of

PhenomeNET: a whole-phenome approach to disease gene discovery

Rare disease Semantic similarity

Venue: Nucleic Acids Research Authors: Robert Hoehndorf, Paul N. Schofield, Georgios V. Gkoutos Abstract Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Phenotype ontologies were developed to capture and compare phenotypes within the context of a single species. Recently, these ontologies were augmented with formal class definitions that may be utilized to integrate phenotypic data and enable the direct comparison of phenotypes between different species. We have developed a method to transform phenotype ontologies into a

Evaluating semantic similarity methods for comparison of text-derived phenotype profiles

Applied Ontology Semantic similarity

Year: 2022 Venue: BMC Medical Informatics and Decision Making Authors: Luke T. Slater, Sophie Russell, Silver Makepeace, Alexander Carberry, Andreas Karwath, John A. Williams, Hilary Fanning, Simon Ball, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1186/s12911-022-01770-4 Abstract Abstract Background Semantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance ‘patient-like me’ analyses, automated coding, differential diagnosis, and outcome prediction. While a large body of work

Effects of Negation and Uncertainty Stratification on Text-Derived Patient Profile Similarity

Applied Ontology Semantic similarity

Year: 2021 Venue: Frontiers in Digital Health Authors: Luke T. Slater, Andreas Karwath, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.3389/fdgth.2021.781227 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 pipelines, their effects on semantic

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

OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction

Applied Ontology Neuro-Symbolic AI Semantic similarity

Year: 2018 Venue: Bioinformatics Authors: Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf DOI: 10.1093/bioinformatics/bty933 Abstract Motivation: Ontologies are widely used in biology for data annotation, integration and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally

Evaluating the effect of annotation size on measures of semantic similarity

Applied Ontology Semantic similarity

Year: 2016 Venue: Proceedings of Bio-Ontologies SIG Authors: Maxat Kulmanov, Robert Hoehndorf Abstract Ontologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products. Here, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of

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

Applied Ontology Drug mechanisms Semantic similarity

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

Notions of similarity for systems biology models

Applied Ontology Semantic similarity

Year: 2018 Venue: Briefings in Bioinformatics Authors: Ron Henkel, Robert Hoehndorf, Tim Kacprowski, Christian Knupfer, Wolfgang Liebermeister, Dagmar Waltemath Abstract Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of 'similarity' may greatly

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

Rare disease 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

A Machine Learning Based Approach for Similarity Search on Biodiversity Knowledge Graphs

Applied Ontology Neuro-Symbolic AI Semantic similarity

Year: 2019 Venue: Biodiversity Information Science and Standards Authors: Claus Weiland, Maxat Kulmanov, Marco Schmidt, Robert Hoehndorf Abstract Mass biodiversity data from scientific collections will be provided by world-wide digitization efforts like iDigBio in the U.S and DiSSCo in Europe. This opens up an increasing amount of data on wild type organisms, which enables the building of large biodiversity knowledge graphs comprising, inter alia, sequence, trait and occurrence data. Knowledge graphs model information in the form of entities and their relationships expressed in good practice

Bio2Vec: Smart analytics infrastructure for the life sciences

Mon, May 18 2026

Applied Ontology Neuro-Symbolic AI Semantic similarity

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

Mon, May 18 2026

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

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

Sun, May 17 2026

Applied Ontology Neuro-Symbolic AI Ontology engineering Semantic similarity

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