Matching Biomedical Ontologies via a Hybrid Graph Attention Network [PDF]
Frontiers in Genetics, 2022 Biomedical ontologies have been used extensively to formally define and organize biomedical terminologies, and these ontologies are typically manually created by biomedical experts. With more biomedical ontologies being built independently, matching them
Peng Wang, Peng Wang, Yunyan Hu
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Evolution of biomedical ontologies and mappings: Overview of recent approaches [PDF]
Computational and Structural Biotechnology Journal, 2016 Biomedical ontologies are heavily used to annotate data, and different ontologies are often interlinked by ontology mappings. These ontology-based mappings and annotations are used in many applications and analysis tasks.
Anika Groß, Cédric Pruski, Erhard Rahm
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JMIR Medical Informatics, 2021 BackgroundOntology matching seeks to find semantic correspondences between ontologies. With an increasing number of biomedical ontologies being developed independently, matching these ontologies to solve the interoperability ...
Peng Wang +3 more
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Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies [PDF]
BMC Medical Informatics and Decision Making, 2020 Background Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations.
Luke T. Slater +2 more
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Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm [PDF]
Biology, 2021 To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two ...
Xingsi Xue, Pei-Wei Tsai, Yucheng Zhuang
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Tackling the challenges of matching biomedical ontologies [PDF]
Journal of Biomedical Semantics, 2018 Background Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves.
Daniel Faria +5 more
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BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies [PDF]
BMC Bioinformatics, 2019 Background Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies.
Andre Lamurias +3 more
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mOWL: Python library for machine learning with biomedical ontologies. [PDF]
Bioinformatics, 2023 Motivation Ontologies contain formal and structured information about a domain and are widely used in bioinformatics for annotation and integration of data.
Zhapa-Camacho F +2 more
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Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies. [PDF]
Database (Oxford), 2022 Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management.
Matentzoglu N +26 more
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Biomedical Ontologies to Guide AI Development in Radiology. [PDF]
J Digit Imaging, 2021 The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must
Filice RW, Kahn CE.
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