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The consistent evolution of ontologies is a major challenge for systems using semantically enriched data, for example, for annotating, indexing, or reasoning. The biomedical domain is a typical example where ontologies, expressed with different formalisms, have been used for a long time and whose dynamic nature requires the regular revision of underlying systems. However, the automatic identification of outdated concepts and proposition of revision actions to update them are still open research questions. Solutions to these problems are of great interest to organizations that manage huge and dynamic ontologies. In this paper, we present an approach for (i) identifying the concepts of an ontology that require revision and (ii) suggesting the type of revision. Our analysis is based on three aspects: structural information encoded in the ontology, relational information gained from external source of knowledge (i.e., PubMed and UMLS) and temporal information derived from the history of the ontology. Our approach aims to evaluate different methods and parameters used by supervised learning classifiers to identify both the set of concepts that need revision, and the type of revision. We applied our approach to four well-known biomedical ontologies/terminologies (ICD-9-CM, MeSH, NCIt and SNOMED CT) and compared our results to similar approaches. Our model shows accuracy ranging from 68% (for SNOMED CT) to 91% (for MeSH), and an average of 71% when considering all datasets together. [ABSTRACT FROM AUTHOR]

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work. [ABSTRACT FROM AUTHOR]

[Display omitted] Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adverse events has increased. Understanding mechanisms of NPDIs is key to preventing or minimizing adverse events. Although biomedical knowledge graphs (KGs) have been widely used for drug-drug interaction applications, computational investigation of NPDIs is novel. We constructed NP-KG as a first step toward computational discovery of plausible mechanistic explanations for pharmacokinetic NPDIs that can be used to guide scientific research. We developed a large-scale, heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature. To construct the KG, biomedical ontologies and drug databases were integrated with the Phenotype Knowledge Translator framework. The semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler, were used to extract semantic predications (subject-relation-object triples) from full texts of the scientific literature related to the exemplar natural products green tea and kratom. A literature-based graph constructed from the predications was integrated into the ontology-grounded KG to create NP-KG. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through KG path searches and meta-path discovery to determine congruent and contradictory information in NP-KG compared to ground truth data. We also conducted an error analysis to identify knowledge gaps and incorrect predications in the KG. The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information compared to ground truth data. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature. NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify known pharmacokinetic interactions between natural products and pharmaceutical drugs mediated by drug metabolizing enzymes and transporters. Future work will incorporate context, contradiction analysis, and embedding-based methods to enrich NP-KG. NP-KG is publicly available at https://doi.org/10.5281/zenodo.6814507. The code for relation extraction, KG construction, and hypothesis generation is available at https://github.com/sanyabt/np-kg. [ABSTRACT FROM AUTHOR]

The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest. Development and evaluation of our approach are described in the context of studying factors that influence intracranial aneurysm (ICA) growth and rupture. We report our experiences in capturing information on 78 individuals with a total of 120 aneurysms. Two example applications related to assessing the relationship between aneurysm size, growth, gene expression modules, and rupture are described. Our work highlights the challenges with respect to data quality, workflow, and analysis of data and its implications toward a learning health system paradigm. [ABSTRACT FROM AUTHOR]

Abstract: Terminologies and ontologies are increasingly prevalent in healthcare and biomedicine. However they suffer from inconsistent renderings, distribution formats, and syntax that make applications through common terminologies services challenging. To address the problem, one could posit a shared representation syntax, associated schema, and tags. We identified a set of commonly-used elements in biomedical ontologies and terminologies based on our experience with the Common Terminology Services 2 (CTS2) Specification as well as the Lexical Grid (LexGrid) project. We propose guidelines for precisely such a shared terminology model, and recommend tags assembled from SKOS, OWL, Dublin Core, RDF Schema, and DCMI meta-terms. We divide these guidelines into lexical information (e.g. synonyms, and definitions) and semantic information (e.g. hierarchies). The latter we distinguish for use by informal terminologies vs. formal ontologies. We then evaluate the guidelines with a spectrum of widely used terminologies and ontologies to examine how the lexical guidelines are implemented, and whether our proposed guidelines would enhance interoperability. [Copyright &y& Elsevier]

Abstract: In this paper we test the hypothesis that a domain reference ontology of the electrocardiogram (ECG) can be employed in an effective manner to achieve semantic integration between ECG data standards. Several standardization initiatives, namely AHA/MIT-BIH (Physionet), SCP-ECG and HL7 aECG, have led to heterogeneous conceptualizations of the ECG domain. We then argue that a shared anchor, the biomedical reality under scrutiny, can effectively support the semantic integration of these ECG standards into a coherent ECG representation for the sake of a unified Electronic Health Record (EHR) model. Our hypothesis is tested by means of an integration experiment that uses, on the one hand, an ECG Ontology and, on the other hand, elicited conceptual models of the ECG standards. As a conclusion, we attest the hypothesis and also provide an integration table depicting correspondence links between entities in the ECG Ontology and elements in the ECG standards. [Copyright &y& Elsevier]

Abstract: There now exists a rich set of ontologies that provide detailed semantics for biological entities of interest. However, there is not (nor should there be) a single source ontology that provides all the necessary semantics for describing biological phenomena. In the domain of physiological biosimulation models, researchers use annotations to convey semantics, and many of these annotations require the use of multiple reference ontologies. Therefore, we have developed the idea of composite annotations that access multiple ontologies to capture the physics-based meaning of model variables. These composite annotations provide the semantic expressivity needed to disambiguate the often-complex features of biosimulation models, and can be used to assist with model merging and interoperability. In this paper, we demonstrate the utility of composite annotations for model merging by describing their use within SemGen, our semantics-based model composition software. More broadly, if orthogonal reference ontologies are to meet their full potential, users need tools and methods to connect and link these ontologies. Our composite annotations and the SemGen tool provide one mechanism for leveraging multiple reference ontologies. [Copyright &y& Elsevier]

Abstract: The Sequence Ontology is an established ontology, with a large user community, for the purpose of genomic annotation. We are reforming the ontology to provide better terms and relationships to describe the features of biological sequence, for both genomic and derived sequence. The SO is working within the guidelines of the OBO Foundry to provide interoperability between SO and the other related OBO ontologies. Here, we report changes and improvements made to SO including new relationships to better define the mereological, spatial and temporal aspects of biological sequence. [Copyright &y& Elsevier]

Abstract: Research on ontologies is becoming widespread in the biomedical informatics community. At the same time, it has become apparent that the challenges of properly constructing and maintaining ontologies have proven more difficult than many workers in the field initially expected. Discovering general, feasible methods has thus become a central activity for many of those hoping to reap the benefits of ontologies. This paper reviews current methods in the construction, maintenance, alignment, and evaluation of ontologies. [Copyright &y& Elsevier]

• MORE is a novel word embedding model for biomedical concepts. • This model combines structured knowledge and corpus-based distributional semantics. • MORE outperforms common corpus-based and ontology-based similarity measures. • Our evaluation showed the efficacy and generalizability of this model. • MORE allows for accurate modeling of concepts in a wide range of applications. Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that concepts are not effectively referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework that incorporates domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text. We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures. In our approach, we propose a new learning objective, modified from the sigmoid cross-entropy objective function. We used two established datasets of semantic similarities among biomedical concept pairs to evaluate the quality of the generated word embeddings. On the first dataset with 29 concept pairs, with similarity scores established by physicians and medical coders, MORE's similarity scores have the highest combined correlation (0.633), which is 5.0% higher than that of the baseline model, and 12.4% higher than that of the best ontology-based similarity measure. On the second dataset with 449 concept pairs, MORE's similarity scores have a correlation of 0.481, based on the average of four medical residents' similarity ratings, and that outperforms the skip-gram model by 8.1%, and the best ontology measure by 6.9%. Furthermore, MORE outperforms three pre-trained transformer-based word embedding models (i.e., BERT, ClinicalBERT, and BioBERT) on both datasets. MORE incorporates knowledge from several biomedical ontologies into an existing corpus-based distributional semantics model, improving both the accuracy of the learned word embeddings and the extensibility of the model to a broader range of biomedical concepts. MORE allows for more accurate clustering of concepts across a wide range of applications, such as analyzing patient health records to identify subjects with similar pathologies, or integrating heterogeneous clinical data to improve interoperability between hospitals. [ABSTRACT FROM AUTHOR]

Created in October 2012, Wikidata is a large-scale, human-readable, machine-readable, multilingual, multidisciplinary, centralized, editable, structured, and linked knowledge-base with an increasing diversity of use cases. Here, we raise awareness of the potential use of Wikidata as a useful resource for biomedical data integration and semantic interoperability between biomedical computer systems. We show the data model and characteristics of Wikidata and explain how this database can be automatically processed by users as well as by computer methods and programs. Then, we give an overview of the medical entities and relations provided by the database and how they can be useful for various medical purposes such as clinical decision support. [ABSTRACT FROM AUTHOR]