@conference {ICBO_2018_4,
	title = {ICBO_2018_4: Comparison of Natural Language Processing Tools for Automatic Gene Ontology Annotation of Scientific Literature},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {<p>Manual curation of scientific literature for ontology-based knowledge representation has proven infeasible and unscalable to the large and growing volume of scientific literature. Automated annotation solutions that leverage text mining and Natural Language Processing (NLP) have been developed to ameliorate the problem of literature curation. These NLP approaches use parsing, syntactical, and lexical analysis of text to recognize and annotate pieces of text with ontology concepts. Here, we conduct a comparison of four state of the art NLP tools at the task of recognizing Gene Ontology concepts from biomedical literature using the Colorado Richly Annotated Full-Text (CRAFT) corpus as a gold standard reference. We demonstrate the use of semantic similarity metrics to compare NLP tool annotations to the gold standard.</p>
},
	keywords = {curation, gene ontology, natural language processing, semantic similarity, text mining},
	url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_4.pdf},
	author = {Lucas Beasley and Prashanti Manda}
}
@conference {ICBO_2018_63,
	title = {ICBO_2018_63: GO-MAP Implements CAFA Tools: Improved Automated Gene Function Annotation for Plants},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {Maize is both a crop species and a model for genetics and genomics research. As such, maize GO annotations produced by the community data projects Gramene and Phytozome are widely used to derive hypotheses for both crop improvement and basic science. Our maize-GAMER project assessed existing maize GO annotations and to implement and test the performance of some of the most commonly used GO prediction tools (i.e., Reciprocal Best Hits and domain presence) alongside three of the top performing tools submitted for evaluation in the CAFA1 (Critical Assessment of protein Function Annotation) competition. All datasets were compared based on F-score using an independent gold-standard dataset (2002 GO annotations for 1,619 genes) provided by MaizeGDB. In addition to producing and comparing these individual GO annotation sets, we also combined the datasets we generated to produce a maize-GAMER aggregate annotation set. Compared to Gramene and Phytozome, the maize-GAMER aggregate set annotates more genes in the maize genome and assigns more GO terms per gene. In addition, the maize-GAMER dataset{\textquoteright}s functional assignments are comparable to Gramene and Phytozome overall (based on F-score). These findings have been published, and the maize-GAMER GO annotations are available via CyVerse and MaizeGDB. Here we review the methods and describe GO-MAP, the pipeline used to generate these datasets. GO-MAP has been containerized to facilitate gene function annotation for other plant proteomes and will be released via CyVerse in the very near future.},
	keywords = {assessment, CAFA, function, gene ontology},
	url = {http://icbo2018.cgrb.oregonstate.edu/},
	author = {Kokulapalan Wimalanathan and Carson Andorf and Iddo Friedberg and Carolyn Lawrence-Dill}
}
