ArticleJournal of biomedical semantics2014
Evolving BioAssay Ontology (BAO): modularization, integration and applications.
Article in Journal of biomedical semantics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Advancing the bioassay ontology through integrated PK/PD and safety pharmacology representation.Journal of biomedical semantics · 2026Article
- Integrating artificial intelligence and manual curation to enhance bioassay annotations in ChEMBL.Journal of cheminformatics · 2026Article
- BioRels' data infrastructure: a scientific schema and exchange standard to transform and enhance biological data sciences.Nucleic acids research · 2025Article
- Kinome-Wide Virtual Screening by Multi-Task Deep Learning.International journal of molecular sciences · 2024Article
- Ontologies for increasing the FAIRness of plant research data.Frontiers in plant science · 2023Review
- Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments.Briefings in bioinformatics · 2022Article
- The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework.Frontiers in toxicology · 2022Article
- MetamORF: a repository of unique short open reading frames identified by both experimental and computational approaches for gene and metagene analyses.Database : the journal of biological databases and curation · 2021Article
- The Life of a Trailing Spouse.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database.Nanomaterials (Basel, Switzerland) · 2020Article
- Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies.ChemMedChem · 2019Article
- Progress in data interoperability to support computational toxicology and chemical safety evaluation.Toxicology and applied pharmacology · 2019Review
- Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures.Molecules (Basel, Switzerland) · 2019Article
- ChEMBL: towards direct deposition of bioassay data.Nucleic acids research · 2019Article
- How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM).Methods in molecular biology (Clifton, N.J.) · 2019Article
- Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices.Methods in molecular biology (Clifton, N.J.) · 2019Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.