Recently developed BioAssay Express (BAE) technology streamlines the conversion of human-readable assay descriptions to computer-readable information. BAE uses semantic standards to mark up bioprotocols, which unleashes the full power of informatics technology on data that could previously only be organized by crude text searching (https://peerj.com/articles/cs-61/).<br/>One of several annotation-support strategies within BAE is the use of machine learning models to provide statistically backed "suggestions" to the curator. We will describe our efforts to complement these models by applying axioms that are embedded within the underlying ontologies, which include the BioAssay Ontology (BAO), Gene Ontology (GO), Drug Target Ontology (DTO) and Cell Line Ontology (CLO). These axioms are a largely untapped resource that can be used to draw connections between biological concepts, thereby improving both curation and quality control. We have already created a resource of 3500 carefully curated assays from the PubChem collection, which we are using as a training set. We will explore how this resource will be used, in conjunction with models and axiom support, to encourage further semantic annotation of publicly available bioassay protocol data. These efforts are timely and important, as such datasets (released by both public and private organizations) are only increasing, with the volume already exceeding the ability of individual scientists to manage productively.<br/>

Adding value to public data using the BioAssay Express: Using semantic web axioms and machine learning to support annotation

254th National Meeting (2017)

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