Visual comprehension and orientation into the COVID-19 CIDO ontology.

The current intensive research on potential remedies and vaccinations for COVID-19 would greatly benefit from an ontology of standardized COVID terms. The Coronavirus Infectious Disease Ontology (CIDO) is the largest among several COVID ontologies, and it keeps growing, but it is still a medium sized ontology. Sophisticated CIDO users, who need more than searching for a specific concept, require orientation and comprehension of CIDO.

Protein design-scapes generated by microfluidic DNA assembly elucidate domain coupling in the bacterial histidine kinase CpxA.

The randomization and screening of combinatorial DNA libraries is a powerful technique for understanding sequence-function relationships and optimizing biosynthetic pathways. Although it can be difficult to predict a priori which sequence combinations encode functional units, it is often possible to omit undesired combinations that inflate library size and screening effort. However, defined library generation is difficult when a complex scan through sequence space is needed.

Overlapping Complex Concepts Have More Commission Errors, Especially in Intensive Terminology Auditing.

SNOMED CT is a large, complex and widely-used terminology. Auditing is part of the life cycle of terminologies. A review of terminologies' content can identify two error categories: commission errors, such as an incorrect parent or attribute relationship, indicating errors in a concept's modeling, and omission errors, such as missing a parent or attribute relationship, representing incomplete modeling of a concept.

Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies.

In previous research, we have demonstrated for a number of ontologies that structurally complex concepts (for different definitions of "complex") in an ontology are more likely to exhibit errors than other concepts. Thus, such complex concepts often become fertile ground for quality assurance (QA) in ontologies. They should be audited first.

Summarizing an Ontology: A "Big Knowledge" Coverage Approach.

Maintenance and use of a large ontology, consisting of thousands of knowledge assertions, are hampered by its scope and complexity. It is important to provide tools for summarization of ontology content in order to facilitate user "big picture" comprehension. We present a parameterized methodology for the semi-automatic summarization of major topics in an ontology, based on a compact summary of the ontology, called an "aggregate partial-area taxonomy", followed by manual enhancement.

Taxonomy-Based Approaches to Quality Assurance of Ontologies.

Ontologies are important components of health information management systems. As such, the quality of their content is of paramount importance. It has been proven to be practical to develop quality assurance (QA) methodologies based on automated identification of sets of concepts expected to have higher likelihood of errors.

Quality assurance of chemical ingredient classification for the National Drug File - Reference Terminology.

The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g.

An empirical analysis of ontology reuse in BioPortal.

Biomedical ontologies often reuse content (i.e., classes and properties) from other ontologies.

From SNOMED CT to Uberon: Transferability of evaluation methodology between similarly structured ontologies.

Objective: To examine whether disjoint partial-area taxonomy, a semantically-based evaluation methodology that has been successfully tested in SNOMED CT, will perform with similar effectiveness on Uberon, an anatomical ontology that belongs to a structurally similar family of ontologies as SNOMED CT.

Method: A disjoint partial-area taxonomy was generated for Uberon. One hundred randomly selected test concepts that overlap between partial-areas were matched to a same size control sample of non-overlapping concepts.

Tracking the Remodeling of SNOMED CT's Bacterial Infectious Diseases.

SNOMED CT's content undergoes many changes from one release to the next. Over the last year SNOMED CT's Bacterial infectious disease subhierarchy has undergone significant editing to bring consistent modeling to its concepts. In this paper we analyze the stated and inferred structural modifications that affected the Bacterial infectious disease subhierarchy between the Jan 2015 and Jan 2016 SNOMED CT releases using a two-phased approach.

Relating Complexity and Error Rates of Ontology Concepts. More Complex NCIt Concepts Have More Errors.

Objectives: Ontologies are knowledge structures that lend support to many health-information systems. A study is carried out to assess the quality of ontological concepts based on a measure of their complexity. The results show a relation between complexity of concepts and error rates of concepts.

Analyzing structural changes in SNOMED CT's Bacterial infectious diseases using a visual semantic delta.

Thousands of changes are applied to SNOMED CT's concepts during each release cycle. These changes are the result of efforts to improve or expand the coverage of health domains in the terminology. Understanding which concepts changed, how they changed, and the overall impact of a set of changes is important for editors and end users.

Introducing the Big Knowledge to Use (BK2U) challenge.

The purpose of the Big Data to Knowledge initiative is to develop methods for discovering new knowledge from large amounts of data. However, if the resulting knowledge is so large that it resists comprehension, referred to here as Big Knowledge (BK), how can it be used properly and creatively? We call this secondary challenge, Big Knowledge to Use. Without a high-level mental representation of the kinds of knowledge in a BK knowledgebase, effective or innovative use of the knowledge may be limited.

A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies.

Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools.

Quality assurance of the gene ontology using abstraction networks.

The gene ontology (GO) is used extensively in the field of genomics. Like other large and complex ontologies, quality assurance (QA) efforts for GO's content can be laborious and time consuming. Abstraction networks (AbNs) are summarization networks that reveal and highlight high-level structural and hierarchical aggregation patterns in an ontology.

Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.

An Abstraction Network is a compact summary of an ontology's structure and content. In previous research, we showed that Abstraction Networks support quality assurance (QA) of biomedical ontologies. The development of an Abstraction Network and its associated QA methodologies, however, is a labor-intensive process that previously was applicable only to one ontology at a time.

Structural measures to track the evolution of SNOMED CT hierarchies.

The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) is an extensive reference terminology with an attendant amount of complexity. It has been updated continuously and revisions have been released semi-annually to meet users' needs and to reflect the results of quality assurance (QA) activities. Two measures based on structural features are proposed to track the effects of both natural terminology growth and QA activities based on aspects of the complexity of SNOMED CT.

Activatable and Cell-Penetrable Multiplex FRET Nanosensor for Profiling MT1-MMP Activity in Single Cancer Cells.

We developed a quantum-dot-based fluorescence resonance energy transfer (QD-FRET) nanosensor to visualize the activity of matrix metalloproteinase (MT1-MMP) at cell membrane. A bended peptide with multiple motifs was engineered to position the FRET pair at a close proximity to allow energy transfer, which can be cleaved by active MT1-MMP to result in FRET changes and the exposure of cell penetrating sequence. Via FRET and penetrated QD signals, the nanosensor can profile cancer cells.

Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network.

Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes.

Abstraction networks for terminologies: Supporting management of "big knowledge".

Objective: Terminologies and terminological systems have assumed important roles in many medical information processing environments, giving rise to the "big knowledge" challenge when terminological content comprises tens of thousands to millions of concepts arranged in a tangled web of relationships. Use and maintenance of knowledge structures on that scale can be daunting. The notion of abstraction network is presented as a means of facilitating the usability, comprehensibility, visualization, and quality assurance of terminologies.

Drug-drug Interaction Discovery Using Abstraction Networks for "National Drug File - Reference Terminology" Chemical Ingredients.

The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology. NDF-RT provides important information about clinical drugs, e.g.

Rapid modulation of droplet composition with pincer microvalves.

Single-layer membrane valves are simple to fabricate and to integrate into microfluidic devices. However, due to their rectangular flow channel geometry, they do not fully seal. Here, we show that liquid flow can be reduced by over 3 orders of magnitude, enabling the contents of forming droplets to be dynamically modulated.

Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies.

Objective: Standards terminologies may be large and complex, making their quality assurance challenging. Some terminology quality assurance (TQA) methodologies are based on abstraction networks (AbNs), compact terminology summaries. We have tested AbNs and the performance of related TQA methodologies on small terminology hierarchies.

A tribal abstraction network for SNOMED CT target hierarchies without attribute relationships.

Objective: Large and complex terminologies, such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), are prone to errors and inconsistencies. Abstraction networks are compact summarizations of the content and structure of a terminology. Abstraction networks have been shown to support terminology quality assurance.

Scalability of abstraction-network-based quality assurance to large SNOMED hierarchies.

Abstraction networks are compact summarizations of terminologies used to support orientation and terminology quality assurance (TQA). Area taxonomies and partial-area taxonomies are abstraction networks that have been successfully employed in support of TQA of small SNOMED CT hierarchies. However, nearly half of SNOMED CT's concepts are in the large Procedure and Clinical Finding hierarchies.