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    Self-normalizing learning on biomedical ontologies using a deep Siamese neural network

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    Type
    Preprint
    Authors
    Smaili, Fatima Z. cc
    Gao, Xin cc
    Hoehndorf, Robert cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2020-04-25
    Permanent link to this record
    http://hdl.handle.net/10754/662661
    
    Metadata
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    Abstract
    Motivation:Ontologies are widely used in biomedicine for the annotation and standardization of data.One of the main roles of ontologies is to provide structured background knowledge within a domain as well as a set of labels, synonyms, and definitions for the classes within a domain. The two types of information provided by ontologies have been extensively exploited in natural language processing and machine learning applications. However, they are commonly used separately, and thus it is unknown if joining the two sources of information can further benefit data analysis tasks. Results:We developed a novel method that applies named entity recognition and normalization methods on texts to connect the structured information in biomedical ontologies with the information contained in natural language. We apply this normalization both to literature and to the natural language information contained within ontologies themselves. The normalized ontologies and text are then used to generate embeddings, and relations between entities are predicted using a deep Siamese neural network model that takes these embeddings as input. We demonstrate that our novel embedding and prediction method using self normalized biomedical ontologies significantly outperforms the state of the art methods in embedding ontologies on two benchmark tasks: prediction of interactions between proteins and prediction of gene disease associations. Our method also allows us to apply ontology based annotations and axioms to the prediction of toxicological effects of chemicals where our method shows superior performance. Our method is generic and can be applied in scenarios where ontologies consisting of both structured information and natural language labels or synonyms are used.
    Citation
    Smaili, F. Z., Gao, X., & Hoehndorf, R. (2020). Self-normalizing learning on biomedical ontologies using a deep Siamese neural network. doi:10.1101/2020.04.23.057117
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2020.04.23.057117
    Additional Links
    http://biorxiv.org/lookup/doi/10.1101/2020.04.23.057117
    https://www.biorxiv.org/content/biorxiv/early/2020/04/25/2020.04.23.057117.full.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1101/2020.04.23.057117
    Scopus Count
    Collections
    Bio-Ontology Research Group (BORG); Preprints; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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