@conference {363, title = {W12-02: TraitBank: semantic integration of biodiversity data from diverse sources}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Easy access to large amounts of biodiversity data has the potential to transform research across the life sciences. However, most of the data generated so far are not easily integrated or repurposed due to a lack of standardization in how scientists talk about the characteristics of organisms, how they describe the context of their observations, and how they document the methods with which the data were collected. TraitBank (eol.org/traitbank) addresses this impediment by linking information aggregated from diverse sources to community-developed ontologies and controlled vocabularies. These post hoc annotations help to organize distributed, heterogeneous knowledge into a lightweight, scalable semantic framework supporting retrieval and reuse for a variety of applications, ranging from large-scale synthetic analyses of biodiversity to linked data products and hands-on data science in the classroom. The TraitBank data store currently holds over 11 million measurements and facts for more than 1.7 million taxa including animals, plants, fungi, and microbes. These data are mobilized from major biodiversity information systems (e.g., International Union for Conservation of Nature, Ocean Biogeographic Information System, Paleobiology Database), open literature repositories (e.g., Dryad, Ecological Archives, Pangaea), label data from natural history collections, and legacy/unpublished data sets. TraitBank subject coverage \ is very broad ranging from distribution, ecology, and life history to morphology and physiology. Data can be downloaded via CSV files or a JSON-LD service. Reuse and redistribution with attribution to the original data sources is encouraged. TraitBank complements taxon or subject-specific knowledge management systems by filling gaps (both in taxonomic and trait space), by recruiting new types of data (e.g., from text-mining, citizen-science, and specimen data digitization efforts) and by integrating knowledge across the entire tree of life and multiple scientific domains. The emerging semantic framework will facilitate data discovery, support queries across data sets, and advance data integration and exchange among projects, thus making more biodiversity data available for use in scientific and policy-oriented applications.

}, author = {Katja Schulz and Jennifer Hammock} } @conference {W11-05, title = {W11-05: Food safety and infectious disease}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {Globalization of food manufacturing, distribution and consumption require food microbiology testing programs to perform high-throughput pathogen diagnostics within a short time frame. Whole-genome sequencing (WGS) can now assemble and type bacterial genomes in near real-time with improved resolution, making WGS a viable diagnostic tool during foodborne illness outbreak investigations. However, genomic data must be combined with epidemiological, clinical, laboratory and other health care data (contextual data) to be meaningfully interpreted for actionable interventions. Canada{\textquoteright}s Integrated Rapid Infectious Disease Analysis (IRIDA) project includes the development of a Genomic Epidemiology Application Ontology (GenEpiO), with the development of standardized food vocabulary as a priority area. Standardized food descriptors are essential for data sharing between public health agencies and health responders, accreditation and reproducibility of WGS pipelines, source attribution and risk assessment.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {William Hsiao} } @conference {W11-02, title = {W11-02: International Efforts in Creating Food Vocabularies}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {A standardised system for classifying and describing food makes it easier to compare data from different sources and perform more detailed types of data analyses. As such, there have been many agency-specific, project-specific and international efforts to create food vocabularies fit for different purposes. Here we describe the different existing and ongoing efforts.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Robert Hoehndorf and Matthew Lange} } @conference {W05-07, title = {W05-07: Ontology-based literature mining of E. coli vaccine-associated gene interaction networks}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. While extensive E. coli vaccine research has been conducted, we are still unable to fully protect ourselves against E. coli infections. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains. The Interaction Network Ontology (INO) includes various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology- based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated gene interactions. Using vaccine-related abstracts, our study identified 11,350 sentences that contain 88 unique INO interaction types and at least two out of 1,781 unique E. coli genes. From this big network, a sub-network that contains 5 E. coli vaccine genes, 62 other E. coli genes, and 25 INO interaction types were also identified. A centrality analysis of these gene interaction networks identified top ranked E. coli genes and INO interaction types. Our INO hierarchical classification also provided an effective way to identify and study the relations and patterns among the 25 interaction types.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Junguk Hur and Arzucan Ozgur and Edison Ong and Yongqun He} } @conference {W05-04, title = {W05-04: LAERTES: An open scalable architecture for linking pharmacovigilance evidence sources with clinical data}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {Integrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. Consistent with these results, there has been a recent call for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks integrating various sources provide some of the input needed for combinatorial signal detection. However, none have been specifically designed to support both regulatory and clinical use cases, nor developed using an open architecture allowing interested scientists to easily add new sources. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES). LAERTES provides an open and scalable architecture for linking evidence sources relevant to investigating the association of drugs with health outcomes of interest (HOIs). Standard terminologies/ontologies are used to represent different entities. For example, drugs and HOIs are represented respectively using RxNorm and SNOMED-CT. At the time of this writing, six evidence sources have been loaded into LAERTES. Also, a prototype evidence exploration user interface and set of Web API services are available. This system operates within a larger software environment provided by the OHDSI clinical research framework.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Richard D. Boyce and Erica A. Voss and Vojtech Huser and Lee Evans and Christian Reich and Jon D. Duke and Nicholas P. Tatonetti and Michel Dumontier and Manfred Hauben and Magnus Wallberg and Lili Peng and Sara Dempster and Yongqun He and Anthony G. Sena and Patrick B. Ryan} } @conference {W05-02, title = {W05-02: Therapeutic Indications and Other Use-case-driven Updates in the Drug Ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {The Drug Ontology (DrOn) is a an OWL2-based representation of drug products and their ingredients, mechanisms of action, strengths, and dose forms, as well as of packaged drug products as represented by United States National Drug Codes (NDCs) [1-3]. The primary goal of DrOn is to support analyses of large, drug-related datasets such as pharmacy claims and EHR data.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {William R. Hogan and Josh Hanna and Amanda Hicks and Samira Amirova and Baxter Bramblett and Matthew Diller and Rodel Enderez and Timothy Modzelewski and Mirela Vasconcelos and Chris Delcher} } @conference {W04-04, title = {W04-04: Who{\textquoteright}s paying and who{\textquoteright}s graying? The organizations and roles associated with insurance policies, funding agencies, and national census data}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {Many social roles are necessarily related to organizations. Employee roles and primary insured roles are just a few examples. However, the relations between such roles and organizations have yet to be systematically worked out in the context of BFO-based ontologies. As an initial step toward developing such a systematic representation, we present use-case driven representations connecting roles to organizations in the context of insurance policies, scientific grants, and U.S. National Census data in OWL/RDF.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {A. Hicks and W.R. Hogan} } @conference {IT705, title = {IT705: A Realist Representation of Social Identity Data}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Social identities merit special treatment in realist ontologies. Their ontological status is unsettled, so we should model them in a manner that is agnostic with respect to their ontological status. Nevertheless, there is a clear criterion for determining whether a specific person has a particular identity, namely, whether that person asserts that they do. This social act forms the basis for a realist representation, not of social identities themselves, but of data about social identities. We report the representation of social identities in the Ontology of Medically Related Social Entities and show that it supports data integration and retrieval.

}, url = {http://ceur-ws.org/Vol-1747/IT705_ICBO2016.pdf}, author = {Amanda Hicks} } @conference {IT702, title = {IT702: To MIREOT or not to MIREOT? A case study of the impact of using MIREOT in the Experimental Factor Ontology (EFO)}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

MIREOT is a mechanism for the selective re-use of individual ontology classes in other ontologies. Designed to minimise effort and to support orthogonality, it is now in widespread use. The consequences for ontology integrity and automated reasoning of using the MIREOT mechanism have so far not been fully assessed. In this paper, we perform an analysis of the Experimental Factor Ontology (EFO), an ontology which uses the MIREOT process to gather classes from a large range of other ontologies. Our study examines the effect of combining EFO with the ontologies it references by actually importing them into the EFO. We then evaluate the consistency and status of the combined ontologies. Through our investigation, we reveal that EFO in combination with all its referenced ontologies is logically inconsistent. Furthermore, when EFO is individually combined with many of the ontologies it references, we find a large number of unsatisfiable classes. These results demonstrate a potential problem within a major ontological ecosystem, and reveals possible disadvantages to the use of the MIREOT system for developing ontologies.

}, url = {http://ceur-ws.org/Vol-1747/IT702_ICBO2016.pdf}, author = {Luke Slater and Georgios Gkoutos and Paul Schofield and Robert Hoehndorf} } @conference {IT701, title = {IT701: A Quality-Assurance Study of ChEBI}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Ontologies are important components of many health-information systems. The Chemical Entities of Biological Interest (ChEBI) ontology has become a standard reference for chemicals appearing in biological contexts. As such, assuring the quality of its content is imperative. In fact, ChEBI has a dedicated Web page at which errors and inconsistencies in its concepts can be reported. A study of the correctness of a random sample of ChEBI concepts is carried out. The results show that quite a large number of ChEBI concepts suffer from some kind of problematic modeling. For example, we found that 15.5\% of the sample concepts exhibited severe errors of commission, including incorrect hierarchical (is a) and lateral relationships. Errors of omission were also prevalent. The overall results of our quality-assurance (QA) study are presented. Suggestions for enhancing the QA processes in place for ChEBI are discussed.

}, url = {http://ceur-ws.org/Vol-1747/IT701_ICBO2016.pdf}, author = {Hasan Yumak and Ling Chen and Michael Halper and Ling Zheng and Yehoshua Perl and Gai Elhanan} } @conference {IT605, title = {IT605: SEPIO: A Semantic Model for the Integration and Analysis of Scientific Evidence}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

The Scientific Evidence and Provenance Information Ontology (SEPIO) was developed to support the description of evidence and provenance information for scientific claims. The core model represents the relationships between claims, their lines of evidence, and the data items that comprise this evidence, as well as the methods, tools, and agents involved in the creation of these artifacts. SEPIO was initially developed to support the data integration and analysis efforts of the Monarch Initiative, where it provides a unified and computable representation of evidence and provenance metadata for genotype-phenotype associations aggregated across diverse model organism and clinical genetics databases. However, additional requirements were collected from diverse community partners in an effort to provide a shared community standard, with a core model that is domain independent and extensible to represent any type of claim and its associated evidence. In this report we describe the structure and principles behind the SEPIO model, and review its applications in support of data integration, curation, knowledge discovery, and manual and computational evaluation of scientific claims. The SEPIO ontology can be found at http://github.com/monarch-initiative/SEPIO-ontology/blob/master/src/ontology/sepio.owl.

}, url = {http://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf}, author = {Matthew Brush and Kent Shefchek and Melissa Haendel} } @conference {IT602, title = {IT602: A Semantic Web Representation of Entire Populations}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Accurately representing demographic realities is a critical component in creating useful, agent-based epidemiological models of infectious disease. Synthetic ecosystems are generated from Census data microsamples in a statistically-sound manner to maintain population-level demographic characteristics. These highly detailed representations of populations are the basis of many advanced simulations of infectious disease epidemics. Creating a standard, machine-readable representation of synthetic ecosystem data would enable easier use and integration with epidemic simulator software. Here we describe an ontology-based representation in Resource Description Framework (RDF) and Web Ontology Language (OWL) of version 1.0 of the 2010 U.S. Synthetic Population database by RTI International. Our representation draws upon applicable classes from several reference ontologies, including the Ontology of Medically Related Social Entities (OMRSE). After failing to find suitable ontological representations of several key data elements in the Synthetic Population dataset, we created new classes in OMRSE for representing employment status, employee roles, workplaces, residences, households, and age measurements. We loaded a test RDF dataset (structured according to ontologies in OWL) of synthetic individuals into a commercial triple store (Stardog) and validated the representation with SPARQL queries.

}, url = {http://ceur-ws.org/Vol-1747/IT602_ICBO2016.pdf}, author = {Daniel Welch and Amanda Hicks and Josh Hanna and William Hogan} } @conference {IT504, title = {IT504: OOSTT: a Resource for Analyzing the Organizational Structures of Trauma Centers and Trauma Systems}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Organizational structures of healthcare organiza-tions has increasingly become a focus of medical research. In the CAF{\'E} project we aim to provide a web-service enabling ontol-ogy-driven comparison of the organizational characteristics of trauma centers and trauma systems. Trauma remains one of the biggest challenges to healthcare systems worldwide. Research has demonstrated that coordinated efforts like trauma systems and trauma centers are key components of addressing this chal-lenge. Evaluation and comparison of these organizations is es-sential. However, this research challenge is frequently com-pounded by the lack of a shared terminology and the lack of ef-fective information technology solutions for assessing and com-paring these organizations. In this paper we present the Ontol-ogy of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological founda-tion to CAF{\'E}{\textquoteright}s web-based questionnaire infrastructure. We present the usage of the ontology in relation to the questionnaire and provide the methods that were used to create the ontology.

}, url = {http://ceur-ws.org/Vol-1747/IT504_ICBO2016.pdf}, author = {Joseph Utecht and John Judkins and Terra Colvin Jr. and J. Neil Otte and Nicholas Rogers and Robert Rose and Maria Alvi and Amanda Hicks and Jane Ball and Stephen M. Bowman and Robert T. Maxson and Rosemary Nabaweesi and Rohit Pradhan and Nels D. Sanddal and M. Eduard Tudoreanu and Robert Winchell and Mathias Brochhausen} } @conference {IT402, title = {IT402: Enhancing the Human Phenotype Ontology for Use by the Layperson}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

In rare or undiagnosed diseases, physicians rely upon genotype and phenotype information in order to compare abnormalities to other known cases and to inform diagnoses. Patients are often the best sources of information about their symptoms and phenotypes. The Human Phenotype Ontology (HPO) contains over 12,000 terms describing abnormal human phenotypes. However, the labels and synonyms in the HPO primarily use medical terminology, which can be difficult for patients and their families to understand. In order to make the HPO more accessible to non-medical experts, we systematically added new synonyms using non-expert terminology (i.e., layperson terms) to the existing HPO classes or tagged existing synonyms as layperson. As a result, the HPO contains over 6,000 classes with layperson synonyms.

}, url = {http://ceur-ws.org/Vol-1747/IT402_ICBO2016.pdf}, author = {Nicole Vasilevsky and Mark Engelstad and Erin Foster and Chris Mungall and Peter Robinson and Sebastian K{\"o}hler and Melissa Haendel} } @conference {IP32, title = {IP32: Enhancing SciENcv through semantic research profile integration with the VIVO-ISF ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

n/a

}, url = {http://ceur-ws.org/Vol-1747/IP32_ICBO2016.pdf}, author = {Marijane White and Matthew Brush and Shahim Essaid and Robin Champieux and Adrienne Zell and Melissa Haendel and Colin Grove and Syeda Momina Tabish and David Eichmann} } @conference {IP24, title = {IP24: An OBI ontology Datum Proof Sheet}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

There are numerous past and current examples of ontology-driven projects that provide auto-generated user interfaces for managing entities and relations, each presenting its own varied and complex data model. Our Datum Proof Sheet application aims to simplify the application development landscape by building community consensus about the way basic categorical, textual and numeric datum fields should be described within the OBOFoundry community of ontologies. The proof sheet shows selected datums (grouped under the context of an OBI {\`O}data representational model{\'O} item) as form inputs on an HTML page, enabling an application ontology{\~O}s contents to be presented to end users (ranging in our case from epidemiologists to software developers) for review without necessarily having a working application to showcase them in. The basic relations and cases necessary for presenting datums in a user interface are mostly satisfied by OBI{\~O}s design, but we introduce a few extra elements to bring more clarity to datum specifications, and to provide user interface term labels and definitions that may differ from those that ontologists prefer in the {\`O}backend{\'O}.

}, url = {http://ceur-ws.org/Vol-1747/IP24_ICBO2016.pdf}, author = {Damion Dooley and Emma Griffiths and Fiona Brinkman and William Hsiao} } @conference {IP21, title = {IP21: FoodON: A Global Farm-to-Fork Food Ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Several resources and standards for indexing food descriptors currently exist, but their content and interrelations are not semantically and logically coherent. Simultaneously, the need to represent knowledge about food is central to many fields including biomedicine and sustainable development. FoodON is a new ontology built to interoperate with the OBO Library and to represent entities which bear a . It encompasses materials in natural ecosystems and food webs as well as human-centric categorization and handling of food. The latter will be the initial focus of the ontology, and we aim to develop semantics for food safety, food security, the agricultural and animal husbandry practices linked to food production, culinary, nutritional and chemical ingredients and processes. The scope of FoodON is ambitious and will require input from multiple domains. FoodON will import or map to material in existing ontologies and standards and will create content to cover gaps in the representation of food-related products and processes. As a robust food ontology can only be created by consensus and wide adoption, we are currently forming an international consortium to build partnerships, solicit domain expertise, and gather use cases to guide the ontologys development. The products of this work are being applied to research and clinical datasets such as those associated with the Canadian Healthy Infant Longitudinal Development (CHILD) study which examines the causal factors of asthma and allergy development in children, and the Integrated Rapid Infectious Disease Analysis (IRIDA) platform for genomic epidemiology and foodborne outbreak investigation.

}, url = {http://ceur-ws.org/Vol-1747/IP21_ICBO2016.pdf}, author = {Emma Griffiths and Damion Dooley and Pier Luigi Buttigieg and Robert Hoehndorf and Fiona Brinkman and William Hsiao} } @conference {IP18, title = {IP18: The ImmPort Antibody Ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

n/a

}, url = {http://ceur-ws.org/Vol-1747/IP18_ICBO2016.pdf}, author = {William Duncan and Travis Allen and Jonathan Bona and Olivia Helfer and Barry Smith and Alan Ruttenberg and Alexander D. Diehl} } @conference {IP14, title = {IP14: Towards designing an ontology encompassing the environment-agriculture-food-diet-health knowledge spectrum for food system sustainability and resilience.}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Feeding 9 billion people is not solely a matter of food, health, nutrition, and the environment. Promoting human health by increasing the sustainability and resilience of food systems requires integrating information from a broad range of disciplines from human nutrition/health systems and agricultural/natural systems to social, financial, physical and political systems. Ontologies serve to specify common terminologies for critical concepts and relationships within these systems, however very few ontologies have been developed with this interdisciplinary focus. Biological ontologies, whether focused on human physiology, soil quality, or nutritional value are only part of the story when it comes to determining linkages throughout the food system that help determine human health and well-being. We seek to build an ontology of food and food systems that encompasses the relevant sustainability issues in their entirety. We have already built an ontology of sustainable sourcing of agricultural raw materials issues and indicators, but aim to expand our ontology to include attributes of resilience, and other issues along the environment-agriculture-food-diet-health knowledge spectrum. Additionally, we aim to create this ontology with the intention of quick usability for the food system decision-maker.

}, url = {http://ceur-ws.org/Vol-1747/IP14_ICBO2016.pdf}, author = {Ruthie Musker and Matthew Lange and Allan Hollander and Patrick Huber and Nathaniel Springer and Courtney Riggle and James Quinn and Thomas Tomich} } @conference {IP08, title = {IP08: Gold-Standard Ontology-Based Annotation of Concepts in Biomedical Text in the CRAFT Corpus: Updates and Extensions}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Ontologies are increasingly used for semantic integration across disparate curated biomedical resources, while gold-standard annotated corpora are needed for accurate training and evaluation of text-mining tools. Bringing together the respective power of these, we created the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of full-length, open-access biomedical journal articles that have been manually annotated both syntactically and semantically with select Open Biomedical Ontologies (OBOs), the first release of which includes \ 100,000 annotations of concepts mentioned in the text of 67 articles and mapped to the classes of eight prominent OBOs. Here we present our continuing work on the corpus, including updated versions of these annotations with newer versions of the ontologies, new annotations made with two additional OBOs, annotations made with newly created extension classes defined in terms of existing classes of the ontologies, and new annotations of roots of prefixed and suffixed words.

}, url = {http://ceur-ws.org/Vol-1747/IP08_ICBO2016.pdf}, author = {Michael Bada and Nicole Vasilevsky and Melissa Haendel and Lawrence Hunter} } @conference {IP07, title = {IP07: The Cell Line Ontology integration and analysis of the knowledge of LINCS cell lines}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Cell lines are crucial to study molecular signatures and pathways, and are widely used in the NIH Common Fund LINCS project. The Cell Line Ontology (CLO) is a community-based ontology representing and classifying cell lines from different resources. To better serve the LINCS research community, from the LINCS Data Portal and ChEMBL, we identified 1,097 LINCS cell lines, among which 717 cell lines were associated with 121 cancer types, and 352 cell line terms did not exist in CLO. To harmonize LINCS cell line representation and CLO, CLO design patterns were slightly updated to add new information of the LINCS cell lines including different database cross-reference IDs. A new shortcut relation was generated to directly link a cell line to the disease of the patient from whom the cell line was originated. After new LINCS cell lines and related information were added to CLO, a CLO subset/view (LINCS-CLOview) of LINCS cell lines was generated and analyzed to identify scientific insights into these LINCS cell lines. This study provides a first time use case on how CLO can be updated and applied to support cell line research from a specific research community or project initiative.

}, url = {http://ceur-ws.org/Vol-1747/IP07_ICBO2016.pdf}, author = {Edison Ong and Jiangan Xie and Zhaohui Ni and Qingping Liu and Yu Lin and Vasileios Stathias and Caty Chung and Stephan Schurer and Yongqun He} } @conference {IP06, title = {IP06: Building a molecular glyco-phenotype ontology to decipher undiagnosed diseases}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Hundreds of rare diseases are due to mutation on genes related to glycans synthesis, degradation or recognition. These glycan-related defects are well described in the literature but largely absent in ontologies and databases of chemical entities and phenotypes, limiting the application of computational methods and ontology-driven tools for characterization and discovery of glycan related diseases. We are curating articles and textbooks in glycobiology related to genetic diseases to inform the content and the structure of an ontology of Molecular Glyco-Phenotypes (MGPO). MGPO will be applied toward use cases including disease diagnosis and disease gene candidate prioritization, using semantic similarity and pattern matching at the glycan level with glycomics data from patient of the Undiagnosed Diseases Network.

}, url = {http://ceur-ws.org/Vol-1747/IP06_ICBO2016.pdf}, author = {Jean-Philippe Gourdine and Thomas Metz and David Koeller and Matthew Brush and Melissa Haendel} } @conference {IP04, title = {IP04: EGO: a biomedical ontology for integrative epigenome representation and analysis}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Epigenomics is crucial to understand biological mechanisms beyond genome DNA. To better represent epigenomic knowledge and support data integration, we developed a prototype Epigenome Ontology (EGO). EGO top level hierarchy and design pattern are provided with a use case illustration. EGO is proposed to be used for statistically analyzing enriched epigenomic features based on given sequence data input using statistical methods.

}, url = {http://ceur-ws.org/Vol-1747/IP04_ICBO2016.pdf}, author = {Yongqun He and Zhaohui Qin and Jie Zheng} } @conference {IP02, title = {IP02: Adding evidence type representation to DIDEO}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

In this poster we present novel development and extension of the Drug-drug Interaction and Drug-drug Interaction Evidence Ontology (DIDEO). We demonstrate how reasoning over this extension of DIDEO can a) automatically create a multi-level hierarchy of evidence types from descriptions of the underlying scientific observations and b) automatically subsume individual evidence items under the correct evidence type. Thus DIDEO will enable evidence items added manually by curators to be automatically categorized into a drug-drug interaction framework with precision and minimal effort from curators. As with all previous DIDEO development this extension is consistent with OBO Foundry principles.

}, url = {http://ceur-ws.org/Vol-1747/IP02_ICBO2016.pdf}, author = {Mathias Brochhausen and Philip E. Empey and Jodi Schneider and William R. Hogan and Richard D. Boyce} } @conference {D202, title = {D201: Ontobull and BFOConvert: Web-based programs to support automatic ontology conversion}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

When a widely reused ontology appears in a new version which is not compatible with older versions, the ontologies reusing it need to be updated accordingly. Ontobull (http://ontobull.hegroup.org) has been developed to automatically update ontologies with new term IRI(s) and associated metadata to take account of such version changes. To use the Ontobull web interface a user is required to (i) upload one or more ontology OWL source files; (ii) input an ontology term IRI mapping; and (where needed) (iii) provide update settings for ontology headers and XML namespace IDs. Using this information, the backend Ontobull Java program automatically updates the OWL ontology files with desired term IRIs and ontology metadata. The Ontobull subprogram BFOConvert supports the conversion of an ontology that imports a previous version of BFO. A use case is pro- vided to demonstrate the features of Ontobull and BFOConvert.

}, url = {http://ceur-ws.org/Vol-1747/D201_ICBO2016.pdf}, author = {Edison Ong and Zuoshuang Xiang and Jie Zheng and Barry Smith and Yongqun He} } @conference {D205, title = {D104: Updates to the AberOWL ontology repository}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

A large number of ontologies have been developed in the biological and biomedical domains, which are mostly expressed in the Web Ontology Language (OWL). These ontologies form a logical foundation for our knowledge in these domains, and they are in widespread use to annotate biomedical and biological datasets. The use of the semantics provided by ontologies requires the use of automated reasoning {\textendash} inferring new knowledge by evaluating the asserted axioms. AberOWL is an ontology repository which utilises an OWL 2 EL reasoner to provide semantic access to classified ontologies. Since our original presentation of the AberOWL framework, we have developed several additional tools and features which enrich its ability to integrate and explore data, make use of the semantic and inferred content of ontologies. Here we present an overview of AberOWL and the enhancements and new features which have been developed since its conception. AberOWL is freely available at http://aber-owl.net.

}, url = {http://ceur-ws.org/Vol-1747/D104_ICBO2016.pdf}, author = {M{\'A} Rodr{\'\i}guez-Garc{\'\i}a and Luke Slater and Imane Boudellioua and Paul Schofield and Georgios Gkoutos and Robert Hoehndorf} } @conference {D102, title = {D102: SPARQL2OWL: towards bridging the semantic gap between RDF and OWL}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Several large databases in biology are now making their information available through the Resource Description Framework (RDF). RDF can be used for large datasets and provides a graph-based semantics. The Web Ontology Language (OWL), another Semantic Web standard, provides a more formal, model- theoretic semantics. While some approaches combine RDF and OWL, for example for querying, knowledge in RDF and OWL is often expressed differently. Here, we propose a method to generate OWL ontologies from SPARQL queries using n-ary relational patterns. Combined with background knowledge from ontologies, the generated OWL ontologies can be used for expressive queries and quality control of RDF data. We implement our method in a a prototype tool available at https://github.com/ bio- ontology- research- group/SPARQL2OWL.

}, url = {http://ceur-ws.org/Vol-1747/D102_ICBO2016.pdf}, author = {Mona Alsharani and Hussein Almashouq and Robert Hoehndorf} } @conference {BT101, title = {BT102: Cycles of Scientific Investigation in Discourse - Machine Reading Methods for the Primary Research Contributions of a Paper}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

We describe a novel approach to machine reading of the primary scientific literature. We treat a description of an experiment as a discourse, viewing a scientific corpus not merely into a collection of documents, but also an extended conversation formed by the collective set of experiments, their introductions and interpretations. This paper introduces this approach as a methodology called {\textquoteleft}Cycles of Scientific Investigation in Discourse{\textquoteright} (CoSID). In CoSID, we capture the central conceptual structure of a paper as a series of nested reasoning loops, composed of passages in results sections, which describe individual research findings. We ground our work with a number of worked examples based on data from the MINTACT and Pathway Logic databases, and illustrate the idea in the context of machine-enable biocuration.

}, url = {http://ceur-ws.org/Vol-1747/BT102_ICBO2016.pdf}, author = {Gully A. Burns and Anita de Waard and Pradeep Dasigi and Eduard H. Hovy} } @conference {BP02, title = {BP02: Disease Named Entity Recognition Using NCBI Corpus}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Named Entity Recognition (NER) in biomedical literature is a very active research area. NER is a crucial component of biomedical text mining because it allows for information retrieval, reasoning and knowledge discovery. Much research has been carried out in this area using semantic type categories, such as fiDNAfl, fiRNAfl, fiproteinsfl and figenesfl. However, disease NER has not received its needed attention yet, specifically human disease NER. Traditional machine learning approaches lack the precision for disease NER, due to their dependence on token level features, sentence level features and the integration of features, such as orthographic, contextual and linguistic features. In this paper a method for disease NER is proposed which utilizes sentence and token level features based on Conditional Random Fields using the NCBI disease corpus. Our system utilizes rich features including orthographic, contextual, affixes, bigrams, part of speech and stem based features. Using these feature sets our approach has achieved a maximum F-score of 94\% for the training set by applying 10 fold cross validation for semantic labeling of the NCBI disease corpus. For testing and development corpus the model has achieved an F-score of 88\% and 85\% respectively.

}, url = {http://ceur-ws.org/Vol-1747/BP02_ICBO2016.pdf}, author = {Thomas Hahn and Hidayat Ur Rahman and Richard Segall} } @conference {BP01, title = {BP01: Ignet: A centrality and INO-based web system for analyzing and visualizing literature-mined networks}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

Ignet (Integrative Gene Network) is a web-based system for dynamical- ly updating and analyzing gene interaction networks mined using all Pub- Med abstracts. Four centrality metrics, namely degree, eigenvector, be- tweenness, and closeness are used to determine the importance of genes in the networks. Different gene interaction types between genes are classified using the Interaction Network Ontology (INO) that classifies interaction types in an ontological hierarchy along with individual keywords listed for each interaction type. An interactive user interface is designed to explore the interaction network as well as the centrality and ontology based net- work analysis. Availability: http://ignet.hegroup.org.

}, url = {http://ceur-ws.org/Vol-1747/BP01_ICBO2016.pdf}, author = {Arzucan Ozgur and Junguk Hur and Zuoshuang Xiang and Edison Ong and Dragomir Radev and Yongqun He} } @conference {BIT103, title = {BIT103: Scalable Text Mining Assisted Curation of PTM Proteoforms in the Protein Ontology}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, abstract = {

The Protein Ontology (PRO) defines protein classes and their interrelationships from the family to the protein form (proteoform) level within and across species. One of the unique contributions of PRO is its representation of post-translationally modified (PTM) proteoforms. However, progress in adding PTM proteoform classes to PRO has been relatively slow due to the extensive manual curation effort required. Here we report an automated pipeline for creation of PTM proteoform classes that leverages two phosphorylation-focused text mining tools (RLIMS-P, which detects mentions of kinases, substrates, and phosphorylation sites, and eFIP, which detects phosphorylation-dependent protein-protein interactions (PPIs)) and our integrated PTM database, iPTMnet. By applying this pipeline, we obtained a set of \ 820 substrate-site pairs that are suitable for automated PRO term generation with literature-based evidence attribution. Inclusion of these terms in PRO will increase PRO coverage of species-specific PTM proteoforms by 50\%. Many of these new proteoforms also have associated kinase and/or PPI information. Finally, we show a phosphorylation network for the human and mouse peptidyl-prolyl cis-trans isomerase (PIN1/Pin1) derived from our dataset that demonstrates the biological complexity of the information we have extracted. Our approach addresses scalability in PRO curation and will be further expanded to advance PRO representation of phosphorylated proteoforms.

}, url = {http://ceur-ws.org/Vol-1747/BIT103_ICBO2016.pdf}, author = {Karen Ross and Darren Natale and Cecilia Arighi and Sheng-Chih Chen and Hongzhan Huang and Gang Li and Jia Ren and Michael Wang and K Vijay-Shanker and Cathy Wu} }