The recently developed BioAssay Express technology streamlines the conversion of human-readable assay descriptions to computer-readable information. BioAssay Express uses public semantic standards (ontologies) to markup 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/). One of several annotation-support strategies within BioAssay Express 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 ontology derived text mining, association rules mining based on existing annotations, and axioms that are embedded within the underlying ontologies. BioAssay Express includes the BioAssay Ontology (BAO), Gene Ontology (GO), Drug Target Ontology (DTO) and Cell Line Ontology (CLO). It can also be extended to handle private ontologies. 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.

Extending machine learning capabilities for semi-automated annotation of biological assays in BioAssay Express

256th National Meeting (2018)

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