@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_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} }