@conference {ICBO_2018_18, title = {ICBO_2018_18: Taking a Dive: Experiments in Deep Learning for Automatic Ontology-based 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 = {

Text mining approaches for automated ontology-based curation of biological and biomedical literature have largely focused on syntactic and lexical analysis along with machine learning. Recent advances in deep learning have shown increased accuracy for textual data annotation. However, the application of deep learning for ontology-based curation is a relatively new area and prior work has focused on a limited set of models. Here, we introduce a new deep learning model/architecture based on combining multiple Gated Recurrent Units (GRU) with a character+word based input. We use data from five ontologies in the CRAFT corpus as a Gold Standard to evaluate our model{\textquoteright}s performance. We also compare our model to seven models from prior work. We use four metrics - Precision, Recall, F1 score, and a semantic similarity metric (Jaccard similarity) to compare our model{\textquoteright}s output to the Gold Standard. Our model resulted in 84\% Precision, 84\% Recall, 83\% F1, and 84\% Jaccard similarity. Results show that our GRU-based model outperforms prior models across all five ontologies. We also observed that character+word inputs result in a higher performance across models as compared to word only inputs. These findings indicate that deep learning algorithms are a promising avenue to be explored for automated ontology-based curation of data. This study also serves as a formal comparison and guideline for building and selecting deep learning models and architectures for ontology-based curation.

}, keywords = {automated curation, deep learning, named entity recognition, natural language processing, Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_18.pdf }, author = {Prashanti Manda and Lucas Beasley and Somya Mohanty} } @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 = {

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.

}, 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_50, title = {ICBO_2018_50: A Natural Language Processing Pipeline to extract phenotypic data from formal taxonomic descriptions with a Focus on Flagellate 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 = {

Assembling large-scale phenotypic datasets for evolutionary and biodiversity studies of plants can be extremely difficult and time consuming. New semi-automated Natural Language Processing (NLP) pipelines can extract phenotypic data from taxonomic descriptions, and their performance can be enhanced by incorporating information from ontologies, like the Plant Ontology (PO) and the Plant Trait Ontology (TO). These ontologies are powerful tools for comparing phenotypes across taxa for large-scale evolutionary and ecological analyses, but they are largely focused on terms associated with flowering plants. We describe a bottom-up approach to identify terms from flagellate plants (including bryophytes, lycophytes, ferns, and gymnosperms) that can be added to existing plant ontologies. We first parsed a large corpus of electronic taxonomic descriptions using the Explorer of Taxon Concepts tool (http://taxonconceptexplorer.org/) and identified flagellate plant specific terms that were missing from the existing ontologies. We extracted new structure and trait terms, and we are currently incorporating the missing structure terms to the PO and modifying the definitions of existing terms to expand their coverage to flagellate plants. We will incorporate trait terms to the TO in the near future.

}, keywords = {flagellate plants, matrices, natural language processing, phenotypic traits, phylogeny, Plant Ontology, Plant Trait Ontology, taxonomic descriptions}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_50.pdf }, author = {Lorena Endara and Gordon Burleigh and Laurel Cooper and Pankaj Jaiswal and Marie-Ang{\'e}lique Laporte and Hong Cui} } @conference {ICBO_2018_60, title = {ICBO_2018_60: Enhancing Semantic Analysis of Pathology Reports}, 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 = {

Pathology reports play an essential role in cancer treatment and research. They contain vital findings about a patient{\textquoteright}s cancer, such as cell histology and molecular markers, that are used to diagnose the type of cancer, determine treatment options, and enhance our understanding of the nature of the disease. In this poster, we present our efforts to better search for meaningful data in pathology reports by enriching our search methods with semantic information.

}, keywords = {named entity recognition, natural language processing, Ontology, pathology report}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_60.pdf }, author = {William Duncan and Philip Whalen and Aditya Muralidharan and Jonathan Kiddy} }