Editorial: biological ontologies and semantic biology

Hancock, John M.

doi:10.3389/fgene.2014.00018

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…In particular, ontologies are used in a wide range of systems biology. Moreover, with the creation of the National Center for Biomedical Ontology (NCBO) in 2006 [25,26,27], an incredible amount of ontologies emerged in the Open Biological and Biomedical Ontologies (OBO) Foundry 3 providing a large variety of bio-ontologies [28]. By the exploration of these bio-ontologies via browsers such the Ontology Lookup Service 4 and the BioPortal 5 , it is remarked that these ontologies treat different parts of systems biology, such as cell types [29,30], molecular functions [31], experimental data analysis [32], identification and annotation of genes [33,34], etc.…”

Section: Biology-related Ontologiesmentioning

confidence: 99%

BNO—An ontology for understanding the transittability of complex biomolecular networks

Ayadi

Zanni-Merk

Beuvron

et al. 2019

Journal of Web Semantics

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Analysis of biological systems is being progressively facilitated by computational tools. Most of these tools are based on qualitative and numerical methods. However, they are not always evident, and there is an increasing need to provide an additional semantic layer. Semantic technologies, especially ontologies, are one of the tools frequently used for this purpose. Indeed, they are indispensable for understanding the semantic knowledge about the operation of cells at a molecular level. We describe here the biomolecular network ontology (BNO) created specially to address the needs of analysing the complex biomolecular network's behaviour. A biomolecular network consists of nodes, denoting cellular entities, and edges, representing interactions among cellular components. The BNO ontology provides a foundation for qualitative simulation of complex biomolecular networks. We test the performance of the proposed BNO ontology by using a real example of a biomolecular network, the bacteriophage T4 gene 32. We illustrate the proposed BNO ontology for reasoning and inferring new knowledge with sets of rules expressed in SWRL. Results demonstrate that the BNO ontology allows to precisely interpret the corresponding semantic context and intelligently model biomolecular networks and their state changes. The Biomolecular Network Ontology (BNO) is freely available at https://github.com/AliAyadi/BNO-ontology-version-1.0.

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Section: Biology-related Ontologiesmentioning

confidence: 99%

BNO—An ontology for understanding the transittability of complex biomolecular networks

Ayadi

Zanni-Merk

Beuvron

et al. 2019

Journal of Web Semantics

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“…Generally, a key requirement for allowing data to be FAIR is the use of open approaches and standardized representation formalisms such as ontologies. Indeed, ontologies have proved crucial in supporting omics data integration [2]. However, since hundreds of biomedical ontologies have been designed and are currently available, a new problem has arisen, i.e., how to integrate different ontological schemes and make them interoperable [3].…”

Section: Introductionmentioning

confidence: 99%

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

et al. 2022

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In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests.

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“…Tripal was first released in 2009 and has since had numerous improvements ( 8 ), the latest of which, Tripal 3 ( 9 ), includes improvements to interoperability, data loading and display and semantic web integration. In particular, usage of ontology-derived Controlled Vocabulary terms (CVterms) for all Tripal content will better position Tripal for semantic discovery and processing of biological data ( 10 ). Tripal 3 also upgrades many of the Drupal concepts, a necessary task as Drupal’s release cycle marches forward.…”

Section: Introductionmentioning

confidence: 99%

“…If the rename method is updated with new code, tests are rerun to ensure no new bugs have been inadvertently created by the new additions. Tests are so helpful that some development practices prescribe writing tests before writing the intended code (Test Driven Development), which can increase test coverage and coding efficiency ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Tripal Developer Toolkit

et al. 2018

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Tripal community database construction toolkit utilizing the content management system Drupal. Tripal is used to make biological, genetic and genomic data more discoverable, shareable, searchable and standardized. As funding for community-level genomics databases declines, Tripal’s open-source codebase provides a means for sites to be built and maintained with a minimal investment in staff and new development. Tripal is ultimately as strong as the community of sites and developers that use it. We present a set of developer tools that will make building and maintaining Tripal 3 sites easier for new and returning users. These tools break down barriers to entry such as setting up developer and testing environments, acquiring and loading test datasets, working with controlled vocabulary terms and writing new Drupal classes.

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Editorial: biological ontologies and semantic biology

Cited by 8 publications

References 15 publications

BNO—An ontology for understanding the transittability of complex biomolecular networks

BNO—An ontology for understanding the transittability of complex biomolecular networks

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

Tripal Developer Toolkit

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