@conference {ICBO_2018_10, title = {ICBO_2018_10: Standardization of the Histopathology Cancer Report: An Ontological Approach}, 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 = {

In recent years, the complexity of cancer pathology reporting has increased significantly. The pathology report covers not only general information such as the presence or absence of cancer, but includes a collection of specific parameters such as tumor size, grade, margin, lymphatic or vascular involvement as well as molecular testing e.g. proteomics and genomics (Figure 1). Soon, biomarkers and immune profiling will play an increasingly more important role in determining the eligibility for particular therapies, along with genetic predisposition and social risk factors. The increased use of digital pathology, which allows streamlined sharing of images, has highlighted the importance of clear communication of the information displayed in the pathology report. In the past years, significant effort has been devoted to redefining the way that histopathology report information is recorded. The College of American Pathologists (CAP) (http://www.cap.org/), a leading organization of board-certified pathologists, introduced synoptic cancer reports, a structured checklist to standardize clinical documentation. Despite continuous improvement and generation of electronic reports, formal representation [1] is still lacking. This lack of standardization limits the ability to integrate pathology information with other genomic and proteomic data and often results in loss of information.

}, keywords = {Cancer, Histopathology Cancer, Histopathology report, Ontology, pathology, Pathology standard, Tumor}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_10.pdf}, author = {Anna Maria Masci and Shannon McCall and Alessandro Racioppi and Helena Judge Ellis and Jihad S. Obeid and Barry Smith and Christian Stoeckert and Jie Zheng} } @conference {ICBO_2018_11, title = {ICBO_2018_11: Planteome \& BisQue: Automating Image Annotation with Ontologies using Deep-Learning Networks}, 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 = {

The field of computer vision has recently experienced tremendous progress due to advances in deep learning. This development holds particular promise in applications for plant research, due to a significant increase in the scale of image data harvesting and a strong field-driven interest in the automated processing of observable phenotypes and visible traits within agronomically important species. Parallel developments have occurred in semantic computing; for example, new ontologies have been initiated to capture plant traits and disease indicators. When combined with existing segmentation capabilities, it is possible to conceptualize software applications that give researchers the ability to analyze large quantities of plant phenotype image data, and to auto-annotate that data with meaningful, computable semantic terminology. We have previously reported on a software application that integrates segmentation and ontologies, but lacked the ability to manage very high-resolution images, and also lacked a database platform to allow for high-volume storage requirements. We have also previously reported our migration of the AISO user-guided segmentation feature to a BisQue (Bio-Image Semantic Query User Environment) module to take advantage of its increased power, ability to scale, secure data management environment, and collaborative software ecosystem. Neither AISO nor our initial BisQue implementation possessed a machine-learning component for interpreting (parts of) images. Plant researchers could benefit greatly from a trained classification model that predicts image annotations with a high degree of accuracy. We have therefore implemented two deep-learning prototypes: a coarse classification module for plant object identification (i.e. flower, fruit) and a fine-grained classification module that focuses on plant traits (e.g. reticulate vs. parallel venation, tip shape). Both classification models return results mapped to ontology terms as a form of annotation enrichment. This current version of the Planteome Deep Segmenter module combines image classification with optional guided segmentation and ontology annotation. We have most recently run the module on local Planteome BisQue client services, and are currently working with CyVerse to install a hosted version on their BisQue client service.

}, keywords = {annotation, convolutional neural networks, deep learning, image analysis, machine learning, Ontology, segmentation}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_11.pdf}, author = {Dimitrios Trigkakis and Justin Preece and Austin Meier and Justin Elser and Kris Kvilekval and Dmitry Fedorov and B.S. Manjunath and Pankaj Jaiswal and Sinisa Todorovic} } @conference {ICBO_2018_14, title = {ICBO_2018_14: Does the Foundational Model of Anatomy ontology provide a knowledgebase for learning and assessment in anatomy education?}, 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 = {Throughout the development of the Foundational Model of Anatomy (FMA) ontology, one of the use cases has been anatomy education. In this work, we examine which types of knowledge taught to anatomy students can be supported by the FMA knowledge base. We first categorize types of anatomical knowledge, then express these patterns in the form {\textquotedblleft}Given ____, state ____{\textquotedblright}. Each of the 33 patterns was evaluated for whether this type of knowledge is compatible with the modeling and scope of the FMA.}, keywords = {anatomy, knowledge representation, medical education, nursing education, Ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Melissa Clarkson and Mark Whipple} } @conference {ICBO_2018_15, title = {ICBO_2018_15: Quality Assurance of Ontology Content Reuse}, 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 = {

Building ontologies is difficult and time-consuming. As such, content reuse has been promoted as an important guiding principle in ontology development. Reusing content from other ontologies can reduce the overall effort involved in new ontology construction and provide better alignment with existing knowledge modeling. However, reuse is not a panacea, and it comes with its own attendant difficulties. In this paper, we investigate some common quality assurance issues associated with reuse, such as duplicated content and versioning problems. Some heuristic-based approaches are proposed for analyzing ontologies for these kinds of quality assurance issues. An analysis is carried out on a sample of the large collection of BioPortal-hosted ontologies, many of which employ reuse. The findings indicate that curators and authors, particularly those new to the reuse process, should be on the alert when developing an ontology with reused content to avoid introducing problems into their own ontologies.

}, keywords = {BioPortal, modeling, Ontology, ontology quality assurance, ontology reuse}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_15.pdf }, author = {Michael Halper and Christopher Ochs and Yehoshua Perl and Sivaram Arabandi and Mark Musen} } @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_21, title = {ICBO_2018_21: From PO to GO and back}, 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 = {Our recent goal for the Plant Ontology (PO) is to have it integrated with then Gene Ontology (GO). The simplest aspect of this is to link morphological and anatomical images of structures in for PO terms with GO terms. This, also, would include images of in situs. By using a process of reciprocal illumination, we will be able to clarify and/or redefine PO terms, in particular. An example of this is the integument in the seed plants. Gymnosperms have one integument; whereas, Angiosperms (flowering plants) have two integuments, i.e., an inner integument surrounded by an outer integument. The question that arises is which of the two integuments in the Angiosperms is the equivalent of the single integument in then Gymnosperms? In Angiosperms, KANADI 1, 2 and 3 genes are expressed in the outer integument but not in the inner integument and in knock out mutants there is no outer integument. KANADI 1, 2 and 3 are found only in the Angiosperm outer integument. Thus, it appears that the inner integument of the Angiosperms is equivalent homologous) to the single integument of the Gymnosperms and the PO terms can be revised accordingly.}, keywords = {angiosperm, GO, gymnosperm, integument, Ontology, PO}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Dennis Stevenson and Cecilia Zumajo} } @conference {ICBO_2018_25, title = {ICBO_2018_25: OOPS: The Ontology of Plant Stress, A semi-automated standardization methodology}, 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 = {

Plant stress traits are an important breeding target for all crop species. Massive amounts of research dollars are spent combating plant diseases and nutrient deficiencies. Often this data is used to achieve a single goal, and then left in a repository to never be used again. As a scientific community, we should be striving to make all publicly funded data reusable, and interoperable. This goal is achievable only through careful annotation using universal data and metadata standards. One such standard is through the use of a standardized vocabulary, or ontology. This paper focuses on producing a semi-automated method to define, and label plant stresses using a combination of web scraping, and ontology design patterns. Standardizing the definitions, and linking plant stress with established hierarchies leverages previous work of developed knowledge bases such as taxonomic classifications and other ontologies.

}, keywords = {automation, data standards, nutrient deficiency, Ontology, plant pathology, Planteome, web scraping}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_25.pdf }, author = {Austin Meier and Marie-Ang{\'e}lique Laporte and Justin Elser and Laurel Cooper and Justin Preece and Pankaj Jaiswal and Jorrit Poelen} } @conference {ICBO_2018_26, title = {ICBO_2018_26: Multi-species Malformation Ontologies}, 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 = {

In the Ontology of Craniofacial Development and Malformation (OCDM) [1] we created malformation ontologies for the human (Craniofacial Human Malformation Ontology [CHMO]), the mouse (Craniofacial Mouse Malformation Ontology [CMMO]) and the zebrafish (Craniofacial Zebrafish Malfomration Ontology [CZMO]) to comprehensively represent in all three species the different pathological entities involved in cranial dysmorphologies, and in particular, the phenotypic and genotypic abnormalities associated with craniofacial microsomias and their correlation to normal and canonical anatomical entities and their corresponding embryological development.

}, keywords = {craniofacial, malformation, Ontology}, author = {Jose Leonardo Mejino and Landon Detwiler and Timothy Cox and Michael Cunningham and James Brinkley} } @conference {ICBO_2018_28, title = {ICBO_2018_28: KTAO: A kidney tissue atlas ontology to support community-based kidney knowledge base development and data integration}, 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 = {

Human kidney has its complex structure and diverse interactions among its cells during homeostasis and in its diseased states. To systematically classify, represent, and integrate kidney gene activity, cell types, cell states, and interstitial components, we developed a Kidney Tissue Atlas Ontology (KTAO). KTAO reuses and aligns with existing ontologies such as the Cell Ontology, UBERON, and Human Phenotype Ontology. KTAO also generates new semantic axioms to logically link terms of entities in different domains. As a first study, KTAO represents over 200 known kidney gene markers and their profiles in different cell types in kidney patients. Such a representation supports kidney knowledge base generation, query, and data integration.

}, keywords = {AKI, atlas, CKD, disease, gene marker, Kidney, KTAO, Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_28.pdf }, author = {Yongqun He and Becky Steck and Edison Ong and Laura Mariani and Chrysta Lienczewski and Ulysses Balis and Matthias Kretzler and Jonathan Himmelfarb and John F. Bertram and Evren Azeloglu and Ravi Iyengar and Deborah Hoshizaki and Sean D. Mooney} } @conference {ICBO_2018_29, title = {ICBO_2018_29: Graph Summary of Ontology-based Annotations}, 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 = {It is desirable to learn about gene function in ever more detail and to communicate the information using the vocabularies of ontologies. However, rich annotations can exacerbate the challenge of intuitive comprehension of that complex knowledge by a human. We developed a graph-based tool called SObA (Summary of Ontology-based Annotations) to simplify and summarize complex annotations while allowing users to drill down into details easily by following the graph paths.In contrast to fixed-category summaries, such as ribbons or slims, for SObA, ontology graphs are trimmed dynamically, strictly dictated by available annotations. By following a set of simple rules, any node and path in the graph that is redundant or less informative is removed. Furthermore, visual cues to important nodes are provided by node color and size, which are determined by the type and amount of annotations. The SObA graph was implemented using the Cytoscape Javascript library, making it easy for users to traverse the graph at multiple levels of detail.SObA can be applied on any annotations that use DAG (directed acyclic graph) ontologies. We have implemented it on C. elegans phenotype annotations and on Gene Ontology annotations of many species. We are exploring the feasibility of applying SObA to summarize annotations of post-composed ontologies such as the mammalian phenotype ontology.}, keywords = {DAG, GO, Ontology, phenotype, summary graph}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Raymond Lee and Juancarlos Chan and Christian Grove and Paul Sternberg} } @conference {ICBO_2018_38, title = {ICBO_2018_38: Expanding the Molecular Glycophenotype Ontology to include model organisms and acquired diseases}, 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 = {

Glycans are an underappreciated class of molecules despite the fact that they are implicated in more than 100 known diseases. We have developed an ontology model that captures glycan abnormalities at the molecular level (glycophenotypes) called the molecular glycophenotype ontology (MGPO). Only 30\% of known glycosyltransferases have been implicated in human genetic disorders of glycosylation. Ortholog glycosyltransferases from model organism can cover relevant biological information on potential human diseases. Thus, extending MGPO to represent additional phenotypes and support annotation of model organism data will help cross-species comparison. Expansion of MGPO will also include annotation of glycophenotypes from acquired diseases.

}, keywords = {diseases, glycans, glycobiology, glycophenotypes, model organisms., Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_38.pdf }, author = {Jean-Philippe Gourdine and Nicole Vasilevsky and Lilly Winfree and Matthew Brush and Melissa Haendel} } @conference {ICBO_2018_42, title = {ICBO_2018_42: ELIXIR Interoperability: Standardisation of identifiers, schemas, and ontologies for scientific communities}, 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 = {ELIXIR (https://www.elixir-europe.org) unites Europe{\textquoteright}s leading life science organisations in managing the increasing volume of data being generated by publicly funded research. ELIXIR operates in a Hub-Node model with the Hub coordinating the activities undertaken by the Nodes via five technical platforms: Compute, Data, Interoperability, Tools, and Training to build a sustainable and foundational framework for Life Science informatics communities. This is done by establishing strategic guidelines underlying scientific operations across ELIXIR{\textquoteright}s twenty-one Node members (as of May 2018). The ELIXIR Interoperability Platform (EIP) has been established to deal with the challenge of delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data across the different levels of complexity and variety of life science data types: across the datasets, data catalogues, data tools and services; across the multitude of biological disciplines and geographical or organisational boundaries. The EIP aims to make available the services and resources needed to make data FAIR. These strategies will be founded on FAIR Principles. Ontologies are a crucial component of the EIP strategy, with OBO Foundry Ontologies recognised as reference resource, and leveraged through resources such as Ontology Lookup Service, which provides additional user services for search and annotation. EIP is currently engaged in addressing specific community-derived use cases where incorporating ontological information is essential: rare disease, marine metagenomics, and plant informatics. These require reference ontologies such as Human Phenotype Ontology (HP), Disease Ontology (DO), Cell Ontology (CL), and UBERON.}, keywords = {Bioschemas, FAIR, identifiers, Interoperability, linked-data, Ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Sirarat Sarntivijai and Nick Juty and Carole Goble and Helen Parkinson and Chris Evelo and Jerry Lanfear and Niklas Blomberg} } @conference {ICBO_2018_43, title = {ICBO_2018_43: Domain Informational Vocabulary Extraction Experiences with Publication Pipeline Integration and Ontology Curation}, 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 = {

We will present updates on an ongoing project DIVE (Domain Informational Vocabulary Extraction), a system designed for extracting domain information from scientific publications. DIVE implements an ensemble of text mining methods for biological entity extraction from article text. DIVE also attempts use the co-occurrence patterns of these entities to establish probable relationships between them. DIVE also features an improved web interface for expert user curation of extracted information, thereby providing a means for a constantly growing and expert curated body of domain information for an article corpus. We also discuss our experiences from successful integration of DIVE with the publishing pipeline for two prominent Plant Biology Journals (The Plant Cell and Plant Physiology) from ASPB (American Society of Plant Biologists). The extracted results are embedded at the end of the final proof of the published article to enhance its accessibility and discoverability. Furthermore, DIVE tracks expert user curation actions on its web interface for future training and improvement of the entity detection algorithm.

}, keywords = {big data, Cyberinfrastructure, DIVE, machine learning, Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_43.pdf }, author = {Amit Gupta and Weijia Xu and Pankaj Jaiswal and Crispin Taylor and Jennifer Regala} } @conference {ICBO_2018_47, title = {ICBO_2018_47: On the statistical sensitivity of semantic similarity metrics}, 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 = {

Measuring the semantic similarity between objects that have been annotated with ontological terms is fundamental to an increasing number of biomedical applications, and several different ontologically-aware semantic similarity metrics are in common use. In some of these applications, only weak semantic similarity is expected for biologically meaningful matches. In such cases, it is important to understand the limits of sensitivity for these metrics, beyond which biologically meaningful matches cannot be reliably distinguished from noise. Here, we present a statistical sensitivity comparison of five common semantic similarity metrics (Jaccard, Resnik, Lin, Jiang \& Conrath, and Hybrid Relative Specificity Similarity) representing three different kinds of metrics (Edge based, Node based, and Hybrid) and four different methods of aggregating individual annotation similarities to estimate similarity between two biological objects - All Pairs, Best Pairs, Best Pairs Symmetric, and Groupwise. We explore key parameter choices that can impact sensitivity. To evaluate sensitivity in a controlled fashion, we explore two different models for simulating data with varying levels of similarity and compare to the noise distribution using resampling. Source data are derived from the Phenoscape Knowledgebase of evolutionary phenotypes. Our results indicate that the choice of similarity metric, along with different parameter choices, can substantially affect sensitivity. Among the five metrics evaluated, we find that Resnik similarity shows the greatest sensitivity to weak semantic similarity. Among the ways to combine pairwise statistics, the Groupwise approach provides the greatest discrimination among values above the sensitivity threshold, while the Best Pairs statistic can be parametrically tuned to provide the highest sensitivity. Our findings serve as a guideline for an appropriate choice and parameterization of semantic similarity metrics, and point to the need for improved reporting of the statistical significance of semantic similarity matches in cases where weak similarity is of interest.

}, keywords = {annotation granularity, curation, Ontology, phenotype, semantic similarity}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_47.pdf }, author = {Prashanti Manda and Todd Vision} } @conference {ICBO_2018_54, title = {ICBO_2018_54: Developing graphic libraries to accompany the Craniofacial Human Ontology}, 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 = {

I describe the development of two graphic libraries to accompany parts of the Craniofacial Human Ontology. One library depicts phenotypes of cleft lip. The other represents development of the human head between 4 and 8 weeks of gestation.

}, keywords = {anatomy, graphics, Ontology, visual representation}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_54.pdf }, author = {Melissa Clarkson} } @conference {ICBO_2018_56, title = {ICBO_2018_56: Towards the Development of an Opioid Misuse Ontology}, 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 = {

Opioid abuse is a major health crisis in the United States, and it is imperative that patients on an abuse trajectory be identified early. Ontologies, with their semantic representations, provide an advantageous framework for use in early identification of opioid misusers. This paper discusses the early-stage development of the Opioid Misuse Ontology (OMO). Existing ontologies from Ontobee and NCBO Bioportal were reviewed. Data representation for opioid use and misuse was modeled using ontologies with terms from existing resources where possible. Several terms were identified that need to be created. Future directions for OMO include development of new classes, creation of an OWL artifact, publication for public comment, and trialing with electronic medical record data to determine how well it identifies opioid misusers.

}, keywords = {misuse, Ontology, opioids}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_56.pdf }, author = {Corey Hayes and Jonathan Bona and Mathias Brochhausen} } @conference {ICBO_2018_57, title = {ICBO_2018_57: An Ontology For Formal Representation Of Medication Adherence-Related Knowledge: Case Study In Breast Cancer}, 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 = {

Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medication-adherence behavior data that poses a challenge to humans and today{\textquoteright}s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior{\textemdash}{\textquotedblleft}Medication Adherence Behavior Ontology{\textquotedblright} (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Prot{\'e}g{\'e}. The MAB-ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories.

}, keywords = {adherence, adjuvant endocrine therapy, adjuvant hormonal therapy, aromatase inhibitors, Ontology, tamoxifen}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_57.pdf }, author = {Suhila Sawesi and Josette Jones and William Duncan} } @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} } @conference {ICBO_2018_61, title = {ICBO_2018_61: Using the Oral Health and Disease Ontology to Study Dental Outcomes in National Dental PBRN Practices}, 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 = {

The use of electronic dental records (EDR) has grown rapidly over the past decade, but the development of methods to use EDR data for research and quality improvement is still in its infancy. In this poster, we present our work in which use the Oral Health and Disease Ontology to integrate EDR data from 99 National Dental PBRN practices in order to study the longevity of posterior composite restorations and the rate of tooth loss following root canal treatments.

}, keywords = {dental procedure, dental research, Ontology, posterior composite restoration, root canal treatment}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_61.pdf }, author = {William Duncan and Thankam Thyvalikakath and Zasim Siddiqui and Michelle LaPradd and Chen Wen and Jim Zheng and Anna Roberts and Daniel Hood and Titus Schleyer and Aparna Manimangalam and Donald Rindal and Mark Jurkovich and Tracy Shea and David Bogacz and Terrence Yu and Jeffry Fellows and Valerie Gordan and Gregg Gilbert} } @conference {ICBO_2018_64, title = {ICBO_2018_64: Development and implementation of the Sickle Cell Disease Ontology}, 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 = {Sickle Cell Disease Ontology (SCDO), is a standardized and human and machine-readable resource that represents terminology about Sickle Cell Disease (SCD), for use by researchers, patients and clinicians. This is a collaborative effort to unambiguously describe all SCD concepts and unifying up-to-date knowledge in sickle cell disease.}, keywords = {Hemoglobinopathy, Ontology, Sickle Cell Disease, Translational research}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Gaston Mazandu and Sickle Cell Disease Ontology Working Group and Nicole Vasilevsky and Ambroise Wonkam and Nicola Mulder} } @conference {ICBO_2018_65, title = {ICBO_2018_65: An ontology-driven data capture and catalogue framework for the development of research and clinical databases in Sickle Cell Disease}, 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 = {We designed an ontology-based Research Electronic Data Capture (REDCap) database of integrated medical and environmental information for Sickle Cell Disease patients. This database integrates data elements in patient clinical records or case report forms (CRFs) across multiple sites and contributes to the unification and standardization of clinical data collection instruments for SCD patient and enhances clinical decision making process and patient care.}, keywords = {Ontology, REDCap, Sickle Cell Disease}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Khuthala Mnika and Sickle Cell Disease Ontology Working Group and Nicole Vasilevsky and Ambroise Wonkam and Nicola Mulder} } @conference {ICBO_2018_67, title = {ICBO_2018_67: Vocabularies, Ontologies, APIs, and Formats for Heterogeneous High Throughput Crop Phenotyping Data}, 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 = {The automation of sensors to measure crops is rapidly increasing in the size and complexity of the data that are available to scientists. The higher resolution, frequency, and diversity of information can enable new scientific discoveries and engineering approaches required to increase the efficiency and sustainability of our agricultural production systems. However, the size and complexity of these data makes it difficult to derive insights and actionable inference. These data often require contextual metadata including plant taxonomy, soil and weather, and agronomic and experimental conditions. While it is easy to agree on the principles of making data FAIR (findable, accessible, interoperable and reusable), it is a more substantial challenge to engineer such data in the absence of clear guidance. The landscape of standards, formats, vocabularies, and ontologies (hereafter, interfaces) that exist is difficult to navigate, and comprehensive specifications that cover the scope of crop phenotyping data are absent. Existing standards and conventions provide only a patchwork of coverage, making it difficult to standardize software and data interfaces. The TERRA Reference phenotyping platform (TERRA REF) is building a suite of open data and software to support advances in the use of crop sensing for breeding and precision agriculture. Although our data are large and diverse, we seek to make it FAIR, useful, and usable to the community of users who represent many science and engineering domains, including computer science, robotics, physics, and biology. This talk will describe our effort to identify, prioritize, and implement interfaces to these data, our efforts to build a community around shared data and software, and our focus on supporting existing software pipelines. It will conclude with a request for feedback on the role of ontologies can play in building a coherent interface to heterogeneous data.}, keywords = {APIs, Crop Plants, Environment, High Throughput Crop Phenotyping, High Throughput Phenotyping, Ontology, phenotype}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {David LeBauer and Craig Willis} } @conference {ICBO_2018_68, title = {ICBO_2018_68: The Planteome Project- Reference Ontologies for Data Integration}, 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 = {The Planteome project is a centralized online plant informatics portal which provides semantic integration of widely diverse datasets with the goal of plant improvement. Traditional plant breeding methods for crop improvement may be combined with next-generation analysis methods and automated scoring of traits and phenotypes to develop improved varieties. The Planteome project (www.planteome.org) develops and hosts a suite of reference ontologies for plants associated with a growing corpus of genomics data. Data annotations linking phenotypes and germplasm to genomics resources are achieved by data transformation and mapping species-specific controlled vocabularies to the reference ontologies. Analysis and annotation tools are being developed to facilitate studies of plant traits, phenotypes, diseases, gene function and expression and genetic diversity data across a wide range of plant species. The project database and the online resources provide researchers tools to search and browse and access remotely via APIs for semantic integration in annotation tools and data repositories providing resources for plant biology, breeding, genomics and genetics.}, keywords = {anatomy, data annotation, development, Ontology, plants, traits}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Laurel Cooper and Pankaj Jaiswal} } @conference {ICBO_2018_74, title = {ICBO_2018_74: The many faces of ontological data}, 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 = {To be added}, keywords = {Bioinformatics, data visualization, Ontology}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Melissa Haendel} } @conference {174, title = {ICBO_2018_75: ECOCORE: An ontology for core ecological concepts}, booktitle = {International Conference on Biomedical Ontology (ICBO 2018)}, year = {2018}, month = {08/06/2018}, publisher = {International Conference on Biological Ontology}, organization = {International Conference on Biological Ontology}, abstract = {

The ontologies in the biological and biomedical domain represent entities from a broad spectrum of the life sciences such as molecules, diseases, phenotypes, and environments. However, until now, ontologies representing ecological concepts, such as trophic modes like autotrophy and insectivory, have been missing. In response to the needs of aggregators such as the Encyclopedia of Life and DataONE, we have developed an ontology of core ecological concepts, ECOCORE. The initial focus of development for ECOCORE is trophic dynamics. An example demonstrating this initial focus is the autotroph class, which is a subclass of organism. It is equivalent to organism and ({\textquoteleft}capable of{\textquoteright} some autotrophy). Autotrophy is a process. ECOCORE development calls on classes from many OBO ontologies, but involves specifically important collaborations with the Environment Ontology, the Behavior Ontology, and the Relations Ontology. ECOCORE development is open to community participation at the GitHub site https://github.com/EcologicalSemantics/ecocore. This presentation will describe ECOCORE and show some of its uses.

}, keywords = {biodiversity, ecology, evolutionary biology, Ontology, ontology development, trophic dynamics}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Anne Thessen and Pier Luigi Buttigieg and Ramona Walls and Jennifer Verdolin and Katja Schulz} }