Apr 25, 2020

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

BioRxiv : the Preprint Server for Biology
F. Z. SmailiRobert Hoehndorf

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 signifi...Continue Reading

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