Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations.

Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high throughput molecular data and generating hypotheses about underlying biological phenomena of experiments. However, the two building blocks of this analysis - the ontology and the annotations - evolve rapidly. We used gene signatures derived from 104 disease analyses to systematically evaluate how enrichment analysis results were affected by evolution of the GO over a decade.

Is the crowd better as an assistant or a replacement in ontology engineering? An exploration through the lens of the Gene Ontology.

Biomedical ontologies contain errors. Crowdsourcing, defined as taking a job traditionally performed by a designated agent and outsourcing it to an undefined large group of people, provides scalable access to humans. Therefore, the crowd has the potential to overcome the limited accuracy and scalability found in current ontology quality assurance approaches.

Using description logics to evaluate the consistency of drug-class membership relations in NDF-RT.

Background: The NDF-RT (National Drug File Reference Terminology) is an ontology, which describes drugs and their properties and supports computerized physician order entry systems. NDF-RT's classes are mostly specified using only necessary conditions and lack sufficient conditions, making its use limited until recently, when asserted drug-class relations were added. The addition of these asserted drug-class relations presents an opportunity to compare them with drug-class relations that can be inferred using the properties of drugs and drug classes in NDF-RT.

Using the wisdom of the crowds to find critical errors in biomedical ontologies: a study of SNOMED CT.

Objectives: The verification of biomedical ontologies is an arduous process that typically involves peer review by subject-matter experts. This work evaluated the ability of crowdsourcing methods to detect errors in SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and to address the challenges of scalable ontology verification.

Methods: We developed a methodology to crowdsource ontology verification that uses micro-tasking combined with a Bayesian classifier.

Crowdsourcing the verification of relationships in biomedical ontologies.

Biomedical ontologies are often large and complex, making ontology development and maintenance a challenge. To address this challenge, scientists use automated techniques to alleviate the difficulty of ontology development. However, for many ontology-engineering tasks, human judgment is still necessary.

An empirically derived taxonomy of errors in SNOMED CT.

Ontologies underpin methods throughout biomedicine and biomedical informatics. However, as ontologies increase in size and complexity, so does the likelihood that they contain errors. Effective methods that identify errors are typically manual and expert-driven; however, automated methods are essential for the size of modern biomedical ontologies.

A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.

Although countless highly penetrant variants have been associated with Mendelian disorders, the genetic etiologies underlying complex diseases remain largely unresolved. By mining the medical records of over 110 million patients, we examine the extent to which Mendelian variation contributes to complex disease risk. We detect thousands of associations between Mendelian and complex diseases, revealing a nondegenerate, phenotypic code that links each complex disorder to a unique collection of Mendelian loci.

Applications of ontology design patterns in biomedical ontologies.

Ontology design patterns (ODPs) are a proposed solution to facilitate ontology development, and to help users avoid some of the most frequent modeling mistakes. ODPs originate from similar approaches in software engineering, where software design patterns have become a critical aspect of software development. There is little empirical evidence for ODP prevalence or effectiveness thus far.