@conference {W14-03, title = {W12-04: The Plant Phenology Ontology for Phenological Data Integration}, 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 = {

Plant phenology the timing of life-cycle events, such as flowering or leafing-out has cascading effects on multiple levels of biological organization, from individuals to ecosystems. Despite the importance of understanding phenology for managing biodiversity and ecosystem services, we are not currently able to address continent-scale phenological responses to anticipated climatic changes. This is not because we lack relevant data. Rather, the problem is that the disparate organizations producing large-scale phenology data are using non-standardized terminologies and metrics during data collection and data processing. Here, we preview the Plant Phenology Ontology, which will provide the standardized vocabulary necessary for annotation of phenological data. We are aggregating, annotating, and analyzing the most significant phenological data sets in the USA and Europe for broad temporal, geographic, and taxonomic analyses of how phenology is changing in relation to climate change.

}, url = {http://icbo2016.cgrb.oregonstate.edu/sites/default/files/W14-03_ICBO2016.pdf}, author = {Brian J. Stucky and John Deck and Ellen Denny and Robert P. Guralnick and Ramona L. Walls and Jennifer Yost} } @conference {W11-01, title = {W11-01: Why we Need 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 = {The need to represent knowledge about food is central to many fields such as health, food safety, nutrition, food allergy, sustainable development, trade, ecosystems etc. Academic, national, provincial and departmental databases are all silos of food terminology and data models. Several resources and standards exist for indexing food descriptors however their content and architecture are not semantically and logically coherent. Here we present a unified approach to developing a Farm-to-Fork food ontology which will facilitate data sharing and interoperability between different health, regulatory, development and research communities worldwide.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Damion Dooley} } @conference {W05-06, title = {W05-06: Hierarchical Sentiment Analysis on HPV Vaccines Related Tweets Using Machine Learning Based Approaches}, 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 order to figure out the reasons behind the low HPV vaccine coverage and come up with corresponding strategies to improve vaccine uptake, understand public opinions on HPV vaccines would be of great help. As a precious and rich data source to analyze public opinions, Twitter is now attracting more attention from medical informatics researchers. In order to use machine learning based methods to automatically track and analyze rapidly-growing HPV vaccine related public opinions on Twitter, we first collected and manually annotated 6,000 related tweets as a gold standard. In order to model the possible sentiments especially the negative sentiments over HPV vaccine on Twitter, a preliminary ontology for hierarchical sentiment classification was built as the annotation scheme. A Kappa annotation agreement at 0.851 was reached. Different features (word n-grams, POS and word clusters) were extracted. Experiments were conducted to test the performance of the different combinations features sets on different levels of classification tasks. Macro F scores at 0.8043 and 0.7552 were reached for top-level classification and finest level classification respectively. The limitations and challenges were also discussed. Our results and analysis indicate that it is feasible to do hierarchical classification tasks on HPV vaccine related tweets using machine learning approaches.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Jingcheng Du and Jun Xu and Hsingyi Song and Xiangyu Liu and Cui Tao} } @conference {W05-05, title = {W05-05: Co-occurrence Analysis of Adverse Events for Typhoid Fever Vaccines}, 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 = {Salmonella enterica serotype Typhi is considered as one of the high-priority potential bioterrorism agents by the Center for Disease Control and Prevention (CDC). Vaccines against Typhi can help with the prophylaxis against typhoid fever. However, little effort has been conducted for post-market safety monitoring of typhoid fever vaccines. In this paper, we proposed a novel network-based computational approach to investigate the co-occurrence relationships among adverse events reported after typhoid fever vaccine (TYP). We focused on association data that were recorded in the Vaccine Adverse Event Reporting System (VAERS) between 1990 and 2014. First, we extracted and summarized adverse event (AE) information from TYP related reports in the VAERS database using Resource Description Framework (RDF). Then, we applied a series of network approaches to the AE co-occurrence network to identify potential associations among these AEs. Specifically, we (1) constructed an AE co-occurrence network after the typhoid fever vaccines; (2) calculated network properties of AE co-occurrence network; (3) identified condensed subnetworks in AE co-occurrence network; and (4) compared MedDRA terms associated with AEs in each subnetwork. We observed that (1) AE co-occurrence network shares the same scale-free network property as other biological networks and social networks; (2) AEs clustered in one subnetwork are usually enriched in certain MedDRA terms.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Yuji Zhang and Jingcheng Du and Cui Tao} } @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 {IT507, title = {IT507: Natural Language Definitions for the Leukemia Knowledge Domain}, 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 creation of natural definitions is a phase of any methodology to build formal ontologies. In order to reach formal definitions, one should first create natural language definitions according to sound principles. We gather a set of principles available in literature and organize them in a list of stages that one can use to create good definitions in natural language. In order to test the set of principles, we conducted a case study in which we create definitions in the domain of cancer, more specifically, definitions for acute myeloid leukemia. After creating and validating the definition of this specific kind of leukemia, we offer remarks about the experiment.

}, url = {http://ceur-ws.org/Vol-1747/IT507_ICBO2016.pdf}, author = {AD De Souza and MB Almeida} } @conference {IT505, title = {IT505: Towards a Standard Ontology Metadata Model}, 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 = {

Bio-ontologies are becoming increasingly important in semantic alignment for data integration, information exchange, and semantic interoperability. Due to the large number of emerging bio-ontologies, it is challenging for ontology for their applications. Therefore, it is important to have a consistent terminology metadata model and a resource for discovering appropriate ontologies or other resource for use in annotating data. This paper aims to seek a common, shareable, and comprehensive method to create, disseminate, and consume metadata about terminology resources.

}, url = {http://ceur-ws.org/Vol-1747/IT505_ICBO2016.pdf}, author = {Hua Min and Stuart Turner and Sherri de Coronado and Brian Davis and Trish Whetzel and Robert R. Freimuth and Harold R. Solbrig and Richard Kiefer and Michael Riben and Grace A. Stafford and Lawrence Wright and Riki Ohira} } @conference {IT503, title = {IT503: Malaria study data integration and information retrieval based on OBO Foundry ontologies}, 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 International Centers of Excellence in Malaria Research (ICEMR) projects involve studies to understand the epidemiology and transmission patterns of malaria in different geographic regions. Two major challenges of integrating data across these projects are: (1) standardization of highly heterogeneous epidemiologic data collected by various ICEMR projects; (2) provision of user-friendly search strategies to identify and retrieve information of interest from the very complex ICEMR data. We pursued an ontology-based strategy to address these challenges. We utilized and contributed to the Open Biological and Biomedical Ontologies to generate a consistent semantic representation of three different ICEMR data dictionaries that included ontology term mappings to data fields and allowed values. This semantic representation of ICEMR data served to guide data loading into a relational database and presentation of the data on web pages in the form of search filters that reveal relationships specified in the ontology and the structure of the underlying data. This effort resulted in the ability to use a common logic for storing and display of data on study participants, their clinical visits, and epidemiological information on their living conditions (dwelling) and geographic location. Users of the Plasmodium Genomics Resource, PlasmoDB, accessing the ICEMR data will be able to search for participants based on environmental factors such as type of dwelling, location or mosquito biting rate, characteristics such as age at enrollment, relevant genotypes or gender and visit data such as laboratory findings, diagnoses, malaria medications, symptoms, and other factors.

}, url = {http://ceur-ws.org/Vol-1747/IT503_ICBO2016.pdf}, author = {Jie Zheng and Jashon Cade and Brian Brunk and David Roos and Chris Stoeckert and San James and Emmanuel Arinaitwe and Bryan Greenhouse and Grant Dorsey and Steven Sullivan and Jane Carlton and Gabriel Carrasco-Escobar and Dionicia Gamboa and Paula Maguina-Mercedes and Joseph Vinetz} } @conference {IT502, title = {IT502: Visualizing the {\textquotedblleft}Big Picture{\textquotedblright} of Change in NCIt{\textquoteright}s Biological Processes}, 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 National Cancer Institute thesaurus (NCIt) is a large and complex ontology. NCIt is frequently updated; a new release is made available approximately every month. Tracking structural changes in NCIt is important for the editors of its content. In this paper we describe a methodology and tool using diff partial-area taxonomies to visually summarize structural changes between two NCIt releases. Diff partial-area taxonomies provide a comprehensible view of the overall impact of the changes. This methodology is illustrated using the Biological Process hierarchy. Specifically, we illustrate how diff partial-area taxonomies reflect change that occurred due to major restructuring of this hierarchy between September 2004 and December 2004. During this time the hierarchy nearly doubled in size and a large portion of the classes were extensively modified. Several kinds of change patterns are identified and discussed.

}, url = {http://ceur-ws.org/Vol-1747/IT502_ICBO2016.pdf}, author = {Yehoshua Perl and Christopher Ochs and Sherri de Coronado and Nicole Thomas} } @conference {IT406, title = {IT406-IP35: The Planteome Project}, 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 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.

}, url = {http://ceur-ws.org/Vol-1747/IT406-IP35_ICBO2016.pdf}, author = {Laurel Cooper and Austin Meier and Justin Elser and Justin Preece and Xu Xu and Ryan Kitchen and Botong Qu and Eugene Zhang and Sinisa Todorovic and Pankaj Jaiswal and Marie-Ang{\'e}lique Laporte and Elizabeth Arnaud and Seth Carbon and Chris Mungall and Barry Smith and Georgios Gkoutos and John Doonan} } @conference {IT405, title = {IT405: Building Concordant Ontologies for Drug Discovery}, 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 this study we demonstrate how we interconnect three different ontologies, the BioAssay Ontology (BAO), LINCS Information FramEwork ontology (LIFEo), and the Drug Target Ontology (DTO). The three ontologies are built and maintained for three different projects: BAO for the BioAssay Ontology Project, LIFEo for the Library of Integrated Network-Based Cellular Signatures (LINCS) project, and DTO for the Illuminating the Druggable Genome (IDG) project. DTO is a new ontology that aims to formally describe drug target knowledge relevant to drug discovery. LIFEo is an application ontology to describe information in the LIFE software system. BAO is a highly accessed NCBO ontology; it has been extended formally to describe several LINCS assays. The three ontologies use the same principle architecture that allows for re-use and easy integration of ontology modules and instance data. Using the formal definitions in DTO, LIFEo, and BAO and data from various resources one can quickly identify disease-relevant and tissue- specific genes, proteins, and prospective small molecules. We show a simple use case example demonstrating knowledge-based linking of life science data with the potential to empower drug discovery.

}, url = {http://ceur-ws.org/Vol-1747/IT405_ICBO2016.pdf}, author = {Hande K{\"u}{\c c}{\"u}k-Mcginty and Saurabh Metha and Yu Lin and Nooshin Nabizadeh and Vasileios Stathias and Dusica Vidovic and Amar Koleti and Christopher Mader and Jianbin Duan and Ubbo Visser and Stephan Schurer} } @conference {IT205, title = {IT205: Data-driven Agricultural Research for Development: A Need for Data Harmonization Via Semantics}, 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 = {

Addressing global challenges to agricultural productivity and profitability increasingly requires access to data from a variety of disciplines, and the ability to easily combine and analyze related data sets. Innovation in agricultural research for development must therefore be mediated by reliable and consistently annotated information resources across disciplinary domains. Leveraging semantics ensures this consistency and ease of reuse, and the global CGIAR Consortium that includes 15 agricultural research for development Centers is attempting to harness this promise through efforts such as its Open Access, Open Data Initiative. CGIAR{\texttrademark}s Crop Ontology project plays a key role in this, and will soon be enhanced by an Agronomy Ontology (AgrO). AgrO is being built to represent traits identified by agronomists and the simulation model variables of the International Consortium for Agricultural Systems Applications (ICASA). Further, it will coordinate its semantics with existing ontologies such as the Environment Ontology (ENVO), Unit Ontology (UO), and Phenotype And Trait Ontology (PATO). Once stable, it is anticipated to address one of the domains temporarily represented in the Sustainable Development Goals Interface Ontology (SDGIO), pertaining to multiple SDGs such as the elimination of hunger and poverty. AgrO will complement existing crop, livestock, and fish ontologies to enable harmonized approaches to data collection, facilitating data sharing and reuse. Further, AgrO will power an Agronomy Management System and fieldbook, similar to the Crop Ontology-based Integrated Breeding Platform (IBP) and fieldbook. There is substantial interest from agronomists and modelers in such a fieldbook to standardize agronomic data collection, and the ontology itself as a means of facilitating hitherto missing linkages with breeding and other data, and enabling wider sharing and reuse of agronomic research data.

}, url = {http://ceur-ws.org/Vol-1747/IT205_ICBO2016.pdf}, author = {Medha Devare and C{\'e}line Aubert and Marie-Ang{\'e}lique Laporte and L{\'e}o Valette and Elizabeth Arnaud and Pier Luigi Buttigieg} } @conference {373, title = {IP34: Plant Reactome: A Resource for Comparative Plant Pathway Analysis}, booktitle = {ICBO and BioCreative 2016}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, OR}, abstract = {

The Plant Reactome database (http://plantreactome.gramene.org/) hosts metabolic, genetic and signaling pathways for several model and crop plant species. The Reactome data model organizes gene products, small molecules and macromolecular interactions into reactions and pathways in the context of their subcellular location to build a systemslevel framework of a plant cell. The Plant Reactome features Oryza sativa (rice) as a reference species, built by importing the RiceCyc metabolic network and curating new metabolic, signaling and genetic pathways. The Plant Reactome database now contains 241 rice reference pathways and orthology-based pathway projections for 58 plant species. Plant Reactome allows users to i) compare pathways across various plant species; ii) query and visualize curated baseline and differential expression data available in the EMBL-EBI{\textquoteright}s Expression Atlas in the context of pathways in the Plant Reactome; and iii) analyze genome-scale expression data and conduct pathway enrichment analysis to enable researchers to identify pathways affected by the stresses or treatments studied in their data sets. Plant Reactome links out to numerous external reference resources, including the gene pages of Gramene, Phytozome, SoyBase, Legume Information System, PeanutBase, Uniprot, as well as ChEBI for small molecules, PubMed for literature supported evidences, and GO for molecular function and biological processes. Users can access/download our data in various formats from our website and via APIs. The presentation will discuss tools for pathway enrichment analysis and homologue pathway comparison, development of the Plant Reactome portal, curation of reference rice pathways, and phylogeny-based analyses of projected pathway annotations. The project is supported by the Gramene database award (NSF IOS-1127112)and the Human Reactome award (NIH: P41 HG003751, ENFIN LSHG-CT-2005-518254, Ontario Research Fund, and EBI Industry Programme).

}, author = {Sushma Naithani and Justin Preece and Parul Gupta and Peter D{\textquoteright}Eustachio and Justin Elser and Antonio Mundao and Joel Weiser and Sheldon McKay and Lincoln Stein and Doreen Ware and Pankaj Jaiswal} } @conference {IP31, title = {IP31: Planteome Gene Annotation Enrichment 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 = {

Annotation enrichment analysis of a gene list helps biologists to identify the potential biological functions associated with it. With the extensions of plant ontology categories, the discovery of significant ontology terms associated with a gene list becomes more and more informative. We introduce a tool to help biologists to find out these terms based on the expanding ontology database of the Planteome project. In addition, we propose some new visualization schemes to help users construct a meaningful interpretation of the results guided by the ontology tree.

}, url = {http://ceur-ws.org/Vol-1747/IP31_ICBO2016.pdf}, author = {Botong Qu and Jaden Diefenbaugh and Eugene Zhang and Justin Elser and Pankaj Jaiswal and Seth Carbon and Christopher Mungall} } @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 {IP03, title = {IP03: Multi-species Ontologies of the Craniofacial Musculoskeletal System}, 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 created the Ontology of Craniofacial Development and Malformation (OCDM) [1] to provide a unifying framework for organizing and integrating craniofacial data ranging from genes to clinical phenotypes from multi-species. Within this framework we focused on spatio-structural representation of anatomical entities related to craniofacial development and malformation, such as craniosynostosis and midface hypoplasia. Animal models are used to support human studies and so we built multi-species ontologies that would allow for cross-species correlation of anatomical information. For this purpose we first developed and enhanced the craniofacial component of the human musculoskeletal system in the Foundational Model of Anatomy Ontology (FMA)[2], and then imported this component, which we call the Craniofacial Human Ontology (CHO), into the OCDM. The CHO was then used as a template to create the anatomy for the mouse, the Craniofacial Mouse Ontology (CMO) as well as for the zebrafish, the Craniofacial Zebrafish Ontology (CZO).

}, url = {http://ceur-ws.org/Vol-1747/IP03_ICBO2016.pdf}, author = {JL Mejino and James Brinkley and Timothy Cox and Landon Detwiler} } @conference {BT304, title = {BT304: BioCconvert: A Conversion Tool Between BioC and PubAnnotation}, 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 = {

BioC is a simple XML data format for text, annotations, and relations. PubAnnotation is a repository of text annotations focused on the life science literature. A conversion tool between BioC XML and the JSON import / export format of PubAnnotation has been developed, BioCconvert. As a demonstration, the Ab3P gold standard abbreviation annotations are being made available through PubAnnotation.

}, url = {http://ceur-ws.org/Vol-1747/BT304_ICBO2016.pdf}, author = {Donald C. Comeau and Rezarta Islamaj Do{\u g}an and Sun Kim and Chih-Hsuan Wei and W. John Wilbur and Zhiyong Lu} } @conference {BT102, title = {BT103: Collaborative Workspaces for Pathway Curation}, 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 present a web based visual biocuration workspace, focusing on curating detailed mechanistic pathways. It was designed as a flexible platform where multiple humans, NLP and AI agents can collaborate in real-time on a common model using an event driven API. We will use this platform for exploring disruptive technologies that can scale up biocuration such as NLP, human-computer collaboration, crowd-sourcing, alternative publishing and gamification. As a first step, we are designing a pilot to include an author-curation step into the scientific publishing, where the authors of an article create formal pathway fragments representing their discovery- heavily assisted by computer agents. We envision that this {\textquotedblleft}micro-curation{\textquotedblright} use-case will create an excellent opportunity to integrate multiple NLP approaches and semi-automated curation.

}, url = {http://ceur-ws.org/Vol-1747/BT103_ICBO2016.pdf}, author = {Funda Durupinar-Babur and MC Siper and Ugur Dogrusoz and Istemi Bahceci and Ozgun Babur and Emek Demir} } @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 {BIT106, title = {BIT106: Use of text mining for Experimental Factor Ontology coverage expansion in the scope of target validation}, 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 = {

Understanding the molecular biology and development of disease plays a key role in drug development. Integrating evidence from different experimental approaches with data available from public resources (such as gene expression level changes and reaction pathways affected by pathogenic mutations) can be a powerful approach for evaluating different aspects of target-disease associations. The application of ontologies is of fundamental importance to effective integration. The Target Validation Platform is a user-friendly interface that integrates such evidences from various resources with the aim of assisting scientists to identify and prioritise drug targets. Currently, the EFO is used as the reference ontology for diseases in the platform, importing terms from existing disease ontologies such as the Human Phenotype Ontology as required. In order to generalize the use of EFO from key target-diseases for wider use, we need to compare the target associated disease coverage in EFO with the scope of other available disease terminology resources. In this study, we address this issue by using text mining and present our initial results.

}, url = {http://ceur-ws.org/Vol-1747/BIT106_ICBO2016.pdf}, author = {Senay Kafkas and Ian Dunham and Helen Parkinson and Johanna Mcentyre} }