@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_31, title = {ICBO_2018_31: Test-driven Ontology Development in Prot{\'e}g{\'e}}, 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 = {

Over the past decade, various quality assurance methodologies have emerged in the field of software engineering for preventing, detecting, and fixing faults and bugs in software. In particular, Test-driven Development (TDD) is now a popular quality assurance technique whereby extensive usage of test cases can be used to enforce the correctness of software artifacts. While testing has made its way into the field of ontology engineering, where some techniques for testing ontologies are now used in prominent biomedical ontology projects, Test-driven Development has yet to achieve significant uptake. In this paper we propose a Logic-Based Test-driven Ontology Development methodology, which takes cues from Test-Driven Development in the field of software engineering. Our hope is that this will encourage a "test-first" approach to ontology engineering and that it will form part of the arsenal available to ontology engineers in order to help them produce and maintain high quality ontologies. Test cases in our framework are represented by simple statements describing expected and/or unwanted logical consequences of an intended ontology. As with Test-driven Development in software engineering, our approach encourages small, frequent iterations in the testing and development life-cycle. We provide and present tool support for our approach in the form of OntoDebug {\textendash}- a plug-in for the ontology editor Prot{\'e}g{\'e}.

}, keywords = {ontology quality assurance, Prot{\'e}g{\'e} Plug-In, Test-Driven Ontology Development}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_31.pdf }, author = {Konstantin Schekotihin and Patrick Rodler and Wolfgang Schmid and Matthew Horridge and Tania Tudorache} } @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_5, title = {ICBO_2018_5: Semantic Integration of Heterogeneous Resources Based on Domain 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 = {Heterogeneity has become an important feature of information resources in the network environment. An integrated approach is needed to achieve the disclosure, association, and retrieval of multiple heterogeneous information resources. The traditional integration methods of information resource can solve the heterogeneous problems in terms of system level, syntax level, structure level, etc. The heterogeneity problems at semantic level will be solved under the support of domain ontology with enhanced semantic. This paper applies domain ontology to the semantic integration of heterogeneous information resources, designs a semantic integration model based on domain ontology, and focus on the problems that should be solved during the implementation of this model. This paper discusses the solutions for the problems and applies them to the process of semantic integration of open resources in the field of plant diversity. The application effect is verified to support the service scenes such as semantic retrieval and knowledge browsing in specific subject areas.}, keywords = {domain ontology, open resources, plant diversity, semantic heterogeneity, semantic integration}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Jinjing Guo and Huiling Ren and Jinxia Huang} } @conference {ICBO_2018_51, title = {ICBO_2018_51: Manually Curated Database of Rice Proteins: a case of digitized experimental data via structured use of 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 = {The {\textquoteleft}Manually Curated Database of Rice Proteins{\textquoteright} (www.genomeindia.org/biocuration) is a data resource based on digitized experimental data on rice proteins. More than 15,000 published experimental datasets (consisting of over 90,000 data-points) from >550 published articles have been digitized in a manual curation exercise. The experimental datasets originate from over 150 different types of experimental techniques. Various combinations of ontologies have been used to represent different aspects of every data point of the experimental datasets. Thus, each data point could be imagined to be represented by an equation consisting of various ontology terms. Each ontology term represents a unique aspect of the data points. In original publication these data sets are represented as an image or a graph which cannot be searched computationally. As a consequence of this curation procedure data from numerous experimental techniques such as enzymatic assays, RT-PCR assays, localization analysis and trait analysis can be rapidly searched from a collection of hundred of curated research publications. The data can either be browsed from various perspectives (tissue, developmental stage, experimental technique, function, treatment etc.) or searched with the help of any ontology term or definition. All the experimental data can be browsed by either the gene name, plant developmental stage, plant tissue, environmental condition (treatment), trait (molecular or biochemical) or gene function. Figure 1. illustrates the interoperability and connectivity of the digitized experimental datasets. As an example one can start browsing with a list of {\textquoteleft}traits{\textquoteright}. Selection of any one trait will give the list of all the genes that have been associated with this trait in published literature. It also shows the actual experimental dataset and the experimental technique that was used for this association. Further selecting any one of these takes the user to the actual digitized data. Thus, the database provides a seamless access and search capability to experimental data that was earlier published as an image or graph and thus could not be searched (accessed) computationally. Continuous efforts are being done to add more and more published datasets to the database and to also extend the exercise to other crops as well.}, keywords = {Digitization, Experimental data, manual curation, rice}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Saurabh Raghuvanshi} } @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_69, title = {ICBO_2018_69: An (ontological) patient perspectiv}, 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 Human Phenotype Ontology (HPO) has become the de facto standard representation of clinical {\textquotedblleft}deep phenotype{\textquotedblright} data for computational comparison of abnormalities and for use in genetic disease diagnostics. Using semantic similarity methods, the HPO is used to match non-exact sets of phenotypic features against known diseases, other patients, and model organisms. Algorithms based on HPO have been implemented into variant prioritization tools and are used by the 100,000 Genomes project, the NIH Undiagnosed Diseases Program/Network, and many other clinics, labs, tools, and databases. However, patient phenotypes can be laborious to capture adequately, and some phenotypes go unnoticed by the clinician (such as those only seen at home). Patients themselves are an eager and untapped source of information about symptoms and phenotypes, however, medical terminology is often perplexing to them, making it difficult to use resources like the HPO. Therefore, to support use of the HPO by patients directly, we have created a {\textquoteleft}layperson{\textquoteright} translation. Approximately 36\% of the HPO terms have at least one layperson synonym, 89\% of the diseases annotated to HPO have at least one HPO annotation with a layperson synonym, and 60\% of all disease annotations refer to HPO terms with lay translations. This coverage suggests that the layperson HPO would be useful in a diagnostic setting despite incomplete coverage. To evaluate the diagnostic utility of this lay translation, we created synthetic profiles ({\textquotedblleft}slim annotations{\textquotedblright}) for each annotated disease in the MONDO disease ontology and compared these slim annotations against the gold standard curated set. We also permuted these profiles by adding or removing annotations to determine how robust the lay annotation profiles might be in the face of missing or noisy data coming from patients. In order to evaluate the lay person profiles, we measured the semantic similarity between HPO gold standard annotations and the derived profiles (with and without noise added). 57\% of profiles scored 80\% similarity or higher, and 75\% of profiles scored 70\% similarity or higher. These results highlight the potential impact that the use of a patient-centered ontology view may have in clinical diagnostics for rare disease patients.}, keywords = {Medicine, Personalized medicine, phenotype, Translational science}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Melissa Haendel and Nicole Vasilevsky and Kayli Rageth and Sebastian Koehler and Peter Robinson and Julie McMurry and Kent Shefchek and Catherine Brownstein and Ingrid Holm and Chris Mungall} } @conference {ICBO_2018_8, title = {ICBO_2018_8: Using Equivalence Axioms from the Mammalian Phenotype Ontology to Facilitate Phenotype and Expression Comparisons}, 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 = {

Comparisons of expression and phenotypes associated with a gene can enhance the understanding of gene function. These comparisons can be difficult to make due to differences in the ontologies used to annotate the data. Using equivalence axioms in the Mammalian Phenotype (MP) ontology and mappings between Uber-anatomy (UBERON) and EMAPA terms MGI has implemented gene expression + phenotype comparison matrices for genes in MGI. These matrices used the shared anatomical concepts between expression and phenotype ontologies to facilitate comparisons between annotations in these two domains.

}, keywords = {expression, mouse, phenotype}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_8.pdf }, author = {Susan Bello and Terry Hayamizu and Cynthia Smith and Martin Ringwald and The Mgi Software Group} } @conference {ICBO_2018_9, title = {ICBO_2018_9: A Prot{\'e}g{\'e} Plug-In for Test-Driven Ontology Development}, 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 = {

Ontology development is a hard and often error-prone process, which requires ontology authors to correctly express their domain knowledge in a formal language. One way to ensure the quality of the resulting ontology is to use test cases, similarly to the best practices in software development. For ontology development, test cases can be specified as statements describing expected and/or unwanted logical consequences of an ontology. However, verifying the test cases and identifying the ontology parts that cause their violation is a complex task, which requires appropriate tool support. In this demo, we present OntoDebug {\textendash} a plug-in for the Prot{\'e}g{\'e} editor {\textendash} that supports test-driven ontology development. OntoDebug can automatically verify whether the ontology satisfies all defined test cases. If any test case is violated, the plug-in assists the user in debugging and repairing the ontology in an interactive way. The plug-in asks a series of questions about the ontology to pinpoint the faulty axioms. Once a fault is repaired, all answers that the author provided in the interactive debugging session may be converted into test cases, thus preserving the additional knowledge, which can be used in future testing of the ontology.

}, keywords = {Fault Detection in Ontologies, Fault Localization in Ontologies, Fault Repair in Ontologies, Ontology Debugging, Prot{\'e}g{\'e} Plug-In, Test-Driven Ontology Development, User Interaction}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_9.pdf }, author = {Konstantin Schekotihin and Patrick Rodler and Wolfgang Schmid and Matthew Horridge and Tania Tudorache} }