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A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using “ontologies.” In information science, an ontology is a systematic method for articulating a “controlled vocabulary” of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine’s Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science.

Extraction of toxicological end points from primary sources is a central component of systematic reviews and human health risk assessments. To ensure optimal use of these data, consistent language should be used for end point descriptions. However, primary source language describing treatment-related end points can vary greatly, resulting in large labor efforts to manually standardize extractions before data are fit for use. To minimize these labor efforts, we applied an augmented intelligence approach and developed automated tools to support standardization of extracted information via application of preexisting controlled vocabularies. We created and applied a harmonized controlled vocabulary crosswalk, consisting of Unified Medical Language System (UMLS) codes, German Federal Institute for Risk Assessment (BfR) DevTox harmonized terms, and The Organization for Economic Co-operation and Development (OECD) end point vocabularies, to roughly 34,000 extractions from prenatal developmental toxicology studies conducted by the National Toxicology Program (NTP) and 6,400 extractions from European Chemicals Agency (ECHA) prenatal developmental toxicology studies, all recorded based on the original study report language. We automatically applied standardized controlled vocabulary terms to 75% of the NTP extracted end points and 57% of the ECHA extracted end points. Of all the standardized extracted end points, about half (51%) required manual review for potential extraneous matches or inaccuracies. Extracted end points that were not mapped to standardized terms tended to be too general or required human logic to find a good match. We estimate that this augmented intelligence approach saved hours of manual effort and yielded valuable resources including a controlled vocabulary crosswalk, organized related terms lists, code for implementing an automated mapping workflow, and a computationally accessible dataset. Augmenting manual efforts with automation tools increased the efficiency of producing a findable, accessible, interoperable, and reusable (FAIR) dataset of regulatory guideline studies. This open-source approach can be readily applied to other legacy developmental toxicology datasets, and the code design is customizable for other study types. https://doi.org/10.1289/EHP13215.

Meta-research is a bourgeoning field studying topics with significant relevance to health sciences librarianship, such as research reproducibility, peer review, and open access. As a discipline that studies research itself and the practices of researchers, meta-research spans disciplines and encompasses a broad spectrum of topics and methods. The breadth of meta-research presents a significant challenge for identifying published meta-research studies. Introducing a subject heading for meta-research in the controlled vocabularies of literature databases has the potential to increase the visibility of meta-research, further advance the field, and expand its impact on research practices. Given the relatively recent designation of meta-research as a field and its expanding use as a term, now is the time to develop appropriate indexing vocabulary. We seek to call attention to the value of meta-research for health sciences librarianship, describe the challenges of identifying meta-research literature with currently available key terms, and highlight the need to establish controlled vocabulary specific to meta-research.

Background The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. Results From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). Conclusion Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.

Background In biological experiments, comprehensive experimental metadata tracking – which comprises experiment, reagent, and protocol annotation with controlled vocabulary from established ontologies – remains a challenge, especially when the experiment involves multiple laboratory scientists who execute different steps of the protocol. Here we describe Annot, a novel web application designed to provide a flexible solution for this task. Results Annot enforces the use of controlled vocabulary for sample and reagent annotation while enabling robust investigation, study, and protocol tracking. The cornerstone of Annot’s implementation is a json syntax-compatible file format, which can capture detailed metadata for all aspects of complex biological experiments. Data stored in this json file format can easily be ported into spreadsheet or data frame files that can be loaded into R ( https://www.r-project.org/ ) or Pandas, Python’s data analysis library ( https://pandas.pydata.org/ ). Annot is implemented in Python3 and utilizes the Django web framework, Postgresql, Nginx, and Debian. It is deployed via Docker and supports all major browsers. Conclusions Annot offers a robust solution to annotate samples, reagents, and experimental protocols for established assays where multiple laboratory scientists are involved. Further, it provides a framework to store and retrieve metadata for data analysis and integration, and therefore ensures that data generated in different experiments can be integrated and jointly analyzed. This type of solution to metadata tracking can enhance the utility of large-scale datasets, which we demonstrate here with a large-scale microenvironment microarray study.

Controlled vocabularies are increasingly used by databases to describe genes and gene products because they facilitate identification of similar genes within an organism or among different organisms. One of The Arabidopsis Information Resource's goals is to associate all Arabidopsis genes with terms developed by the Gene Ontology Consortium that describe the molecular function, biological process, and subcellular location of a gene product. We have also developed terms describing Arabidopsis anatomy and developmental stages and use these to annotate published gene expression data. As of March 2004, we used computational and manual annotation methods to make 85,666 annotations representing 26,624 unique loci. We focus on associating genes to controlled vocabulary terms based on experimental data from the literature and use The Arabidopsis Information Resource-developed PubSearch software to facilitate this process. Each annotation is tagged with a combination of evidence codes, evidence descriptions, and references that provide a robust means to assess data quality. Annotation of all Arabidopsis genes will allow quantitative comparisons between sets of genes derived from sources such as microarray experiments. The Arabidopsis annotation data will also facilitate annotation of newly sequenced plant genomes by using sequence similarity to transfer annotations to homologous genes. In addition, complete and up-to-date annotations will make unknown genes easy to identify and target for experimentation. Here, we describe the process of Arabidopsis functional annotation using a variety of data sources and illustrate several ways in which this information can be accessed and used to infer knowledge about Arabidopsis and other plant species.

Background Biomedical literature concerns a wide range of concepts, requiring controlled vocabularies to maintain a consistent terminology across different research groups. However, as new concepts are introduced, biomedical literature is prone to ambiguity, specifically in fields that are advancing more rapidly, for example, drug design and development. Entity linking is a text mining task that aims at linking entities mentioned in the literature to concepts in a knowledge base. For example, entity linking can help finding all documents that mention the same concept and improve relation extraction methods. Existing approaches focus on the local similarity of each entity and the global coherence of all entities in a document, but do not take into account the semantics of the domain. Results We propose a method, PPR-SSM, to link entities found in documents to concepts from domain-specific ontologies. Our method is based on Personalized PageRank (PPR), using the relations of the ontology to generate a graph of candidate concepts for the mentioned entities. We demonstrate how the knowledge encoded in a domain-specific ontology can be used to calculate the coherence of a set of candidate concepts, improving the accuracy of entity linking. Furthermore, we explore weighting the edges between candidate concepts using semantic similarity measures (SSM). We show how PPR-SSM can be used to effectively link named entities to biomedical ontologies, namely chemical compounds, phenotypes, and gene-product localization and processes. Conclusions We demonstrated that PPR-SSM outperforms state-of-the-art entity linking methods in four distinct gold standards, by taking advantage of the semantic information contained in ontologies. Moreover, PPR-SSM is a graph-based method that does not require training data. Our method improved the entity linking accuracy of chemical compounds by 0.1385 when compared to a method that does not use SSMs.

The evolution of digital media has increased the number of crimes committed using digital equipment. This has led to the evolution of the computer forensics area to digital forensics (DF). Such an area aims to analyze information through its main phases of identification, collection, organization, and presentation (reporting). As this area has evolved, many techniques have been developed, mainly focusing on the formalization of terminologies and concepts for providing a common vocabulary comprehension. This has demanded efforts on several initiatives, such as the definition of ontologies, which are a means to identify the main concepts of a given area. Hence, the existing literature provides several ontologies developed for supporting the DF area. Therefore, to identify and analyze the existing ontologies for DF, this paper presents a systematic literature review (SLR) in which primary studies in the literature are studied. This SLR resulted in the identification of ontology building methodologies, ontology types, feasibility points, evaluation/assessment methods, and DF phases and subareas ontologies have supported. These results were based on the analysis of 29 ontologies that aided in answering six research questions. Another contribution of this paper is a set of recommendations on further ontology-based support of DF investigation, which can guide researchers and practitioners in covering existing research gaps.

Advances in high-throughput experimental techniques in the past decade have enabled the explosive increase of omics data, while effective organization, interpretation, and exchange of these data require standard and controlled vocabularies in the domain of biological and biomedical studies. Ontologies, as abstract description systems for domain-specific knowledge composition, hence receive more and more attention in computational biology and bioinformatics. Particularly, many applications relying on domain ontologies require quantitative measures of relationships between terms in the ontologies, making it indispensable to develop computational methods for the derivation of ontology-based semantic similarity between terms. Nevertheless, with a variety of methods available, how to choose a suitable method for a specific application becomes a problem. With this understanding, we review a majority of existing methods that rely on ontologies to calculate semantic similarity between terms. We classify existing methods into five categories: methods based on semantic distance, methods based on information content, methods based on properties of terms, methods based on ontology hierarchy, and hybrid methods. We summarize characteristics of each category, with emphasis on basic notions, advantages and disadvantages of these methods. Further, we extend our review to software tools implementing these methods and applications using these methods.