An Alignment-Based Implementation of a Holistic Ontology Integration Method.
Ontology highlight
ABSTRACT:
SUBMITTER: Osman I
PROVIDER: S-EPMC8374672 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license.KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.
| S-EPMC4448321 | biostudies-literature
Project description:Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.
| S-EPMC6661928 | biostudies-literature
Project description:Data from the electronic medical record comprise numerous structured but uncoded elements, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of relevant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.
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Project description:Ontologies are widely used in the biomedical community for annotation and integration of databases. Formal definitions can relate classes from different ontologies and thereby integrate data across different levels of granularity, domains and species. We have applied this methodology to the Ascomycete Phenotype Ontology (APO), enabling the reuse of various orthogonal ontologies and we have converted the phenotype associated data found in the SGD following our proposed patterns. We have integrated the resulting data in the cross-species phenotype network PhenomeNET, and we make both the cross-species integration of yeast phenotypes and a similarity-based comparison of yeast phenotypes across species available in the PhenomeBrowser. Furthermore, we utilize our definitions and the yeast phenotype annotations to suggest novel functional annotations of gene products in yeast.
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Project description:BackgroundAlignment (i.e., the process of creating fit between elements of the inner and outer context of an organization or system) in conjunction with implementation of an evidence-based intervention (EBI) has been identified as important for implementation outcomes. However, research evidence has so far not been systematically summarized. The aim of this scoping review is therefore to create an overview of how the concept of alignment has been applied in the EBI implementation literature to provide a starting point for future implementation efforts in health care.MethodsWe searched for peer-reviewed English language articles in four databases (MEDLINE, Cinahl, Embase, and Web of Science) published between 2003 and 2019. Extracted data were analyzed to address the study aims. A qualitative content analysis was carried out for items with more extensive information. The review was reported according to the preferred reporting items for systematic reviews and meta-analyses extension for scoping review (PRISMA-ScR) guidelines.ResultsThe database searches yielded 3629 publications, of which 235 were considered potentially relevant based on the predetermined eligibility criteria, and retrieved in full text. In this review, the results of 53 studies are presented. Different definitions and conceptualizations of alignment were found, which in general could be categorized as structural, as well as social, types of alignments. Whereas the majority of studies viewed alignment as important to understand the implementation process, only a few studies actually assessed alignment. Outcomes of alignment were focused on either EBI implementation, EBI sustainment, or healthcare procedures. Different actors were identified as important for creating alignment and five overall strategies were found for achieving alignment.ConclusionsAlthough investigating alignment has not been the primary focus of studies focusing on EBI implementation, it has still been identified as an important factor for the implementation success. Based on the findings from this review, future research should incorporate alignment and put a stronger emphasize on testing the effectiveness of alignment related to implementation outcomes.
| S-EPMC8554825 | biostudies-literature
Project description:Estimating free energy differences by computer simulation is useful for a wide variety of applications such as virtual screening for drug design and for understanding how amino acid mutations modify protein interactions. However, calculating free energy differences remains challenging and often requires extensive trial and error and very long simulation times in order to achieve converged results. Here, we present an implementation of the adaptive integration method (AIM). We tested our implementation on two molecular systems and compared results from AIM to those from a suite of other methods. The model systems tested here include calculating the solvation free energy of methane, and the free energy of mutating the peptide GAG to GVG. We show that AIM is more efficient than other tested methods for these systems, that is, AIM results converge to a higher level of accuracy and precision for a given simulation time.
| S-EPMC7808261 | biostudies-literature
Project description:BackgroundIntegration and exploration of data obtained from genome wide monitoring technologies has become a major challenge for many bioinformaticists and biologists due to its heterogeneity and high dimensionality. A widely accepted approach to solve these issues has been the creation and use of controlled vocabularies (ontologies). Ontologies allow for the formalization of domain knowledge, which in turn enables generalization in the creation of querying interfaces as well as in the integration of heterogeneous data, providing both human and machine readable interfaces.ResultsWe designed and implemented a software tool that allows investigators to create their own semantic model of an organism and to use it to dynamically integrate expression data obtained from DNA microarrays and other probe based technologies. The software provides tools to use the semantic model to postulate and validate of hypotheses on the spatial and temporal expression and function of genes. In order to illustrate the software's use and features, we used it to build a semantic model of rice (Oryza sativa) and integrated experimental data into it.ConclusionIn this paper we describe the development and features of a flexible software application for dynamic gene expression data annotation, integration, and exploration called Orymold. Orymold is freely available for non-commercial users from http://www.oryzon.com/media/orymold.html.
| S-EPMC2696451 | biostudies-literature
Project description:IntroductionHospital-based practices today remain predominantly disease-oriented, focusing on individual clinical specialties with less visibility on a comprehensive picture of each patient's health needs. To tackle the challenge of growing multimorbidity worldwide, practices without disease-specific focus have shown better integration of services. However, as we move away from the familiar disease-specific approaches of care delivery, many of us are still learning how to implement generalist care in a cost-effective manner.MethodsThis mixed-method case study, which centred on a specialist-led General Medicine model implemented at an acute hospital in Singapore, aimed to (1) quantitatively summarise its clinical outcomes, and (2) qualitatively describe the challenges and lessons gathered from the pragmatic implementation of the care model. Quantitative hospital data were extracted from databases and summarised. Qualitative staff-reported experiences and insights were gathered through semi-structured interviews and analysed using thematic analysis.ResultsQuantitative findings revealed that the generalist care model was implemented with high fidelity, where more than 75% of patients admitted were placed under General Medicine's or General Surgery's care. The mean length of stay was 2.6 days, and the 30-day post-discharge readmission rate was 15%. Inpatient mortality rate was found to be 2.8%, and the average gross hospitalisation bill amounted to SGD3,085.30. For qualitative findings, themes concerning feasibility and operational aspects of the implementation were grouped into categories- (1) Feasibility of 'One Care Team' approach, (2) Enablers required for meaningful generalist care, (3) Challenges surrounding information sharing, (4) Lack of integration with the community to facilitate care transition, and (5) Evolving roles of self-management. The findings were rich, with some being identified as barriers that could benefit from system-level de-constraining.DiscussionThis case study was an illustration of our pursuit for an integrated solution to rising prevalence of multimorbidity. While quantitative findings indicated that a pivot towards General Medicine might be possible, data also revealed gaps in clinical outcomes, especially in readmission rates. These findings corroborated with much of the lessons and challenges gathered from qualitative interviews, specifically surrounding the lack of receptacles in the community to facilitate care transition, training, and competency of generalists in holistic management of complex multimorbid cases, as well as inadequate infrastructure to allow information sharing between providers. Thus, a multi-pronged approach might be required to develop a new and sustainable care model for patients with multimorbidity in the long run. In the short to medium transitional period, nonetheless, the specialist-led General Medicine care model demonstrated might be a viable interim approach, especially in circumstances where trained medical generalists remained limited.
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Project description:The Gene Ontology (GO) (http://www.geneontology.org/) contains a set of terms for describing the activity and actions of gene products across all kingdoms of life. Each of these activities is executed in a location within a cell or in the vicinity of a cell. In order to capture this context, the GO includes a sub-ontology called the Cellular Component (CC) ontology (GO-CCO). The primary use of this ontology is for GO annotation, but it has also been used for phenotype annotation, and for the annotation of images. Another ontology with similar scope to the GO-CCO is the Subcellular Anatomy Ontology (SAO), part of the Neuroscience Information Framework Standard (NIFSTD) suite of ontologies. The SAO also covers cell components, but in the domain of neuroscience.Recently, the GO-CCO was enriched in content and links to the Biological Process and Molecular Function branches of GO as well as to other ontologies. This was achieved in several ways. We carried out an amalgamation of SAO terms with GO-CCO ones; as a result, nearly 100 new neuroscience-related terms were added to the GO. The GO-CCO also contains relationships to GO Biological Process and Molecular Function terms, as well as connecting to external ontologies such as the Cell Ontology (CL). Terms representing protein complexes in the Protein Ontology (PRO) reference GO-CCO terms for their species-generic counterparts. GO-CCO terms can also be used to search a variety of databases.In this publication we provide an overview of the GO-CCO, its overall design, and some recent extensions that make use of additional spatial information. One of the most recent developments of the GO-CCO was the merging in of the SAO, resulting in a single unified ontology designed to serve the needs of GO annotators as well as the specific needs of the neuroscience community.
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Project description:The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.
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