@conference {W11-07, title = {W11-07: IC3-Foods: An infrastructure for the next generation internet of food systems, food, and health.}, 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 = {IC3-FOODS, The International Conference/Consortium/Center for Food Ontology, Operability, Data and Semantics, is a new effort at UC Davis, assembling ontological and semantic infrastructure components for next generation internet of food and health. It consists of 3 specific efforts: i) The International Conference for FOODS assembles stakeholders desiring to integrate data and informatics systems currently residing along the Environment<=>Ag<=>Food<=>Diet<=>Health knowledge spectrum, into the: ii) International Consortium of FOODS, which maintains membership of representative stakeholders from academia, industry and (non-)governmental organizations to guide research priorities and development trajectories carried out by: iii) The International Center for FOODS, whose mission consists of hosting the IConference--FOODS, administering the I-Consortium-FOODS, and designing, assembling, and coordinating ontological and infrastructure underpinnings for the emerging semantic web of ag, food, diet, and health.}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Matthew Lange} } @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 {W05-06, 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 {350, title = {W01-04: Telling a genome{\textquoteright}s story graphically}, 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}, address = {Corvallis, Oregon, USA}, abstract = {

Scientific research is inherently a collaborative task; a dialog among different researchers to reach a consensus understanding of the underlying biology. Information graphics facilitate this dialog because humans are visually wired and can absorb well-executed graphics more quickly than they can absorb the written word or columns of numbers. Visual representations communicate complex ideas quickly and clearly.

This talk will briefly introduce basic principles of designing interactive graphics, with examples from the genomics community illustrating various points. It will also discuss the lessons learned with our own annotation tools: PAINT{\textemdash}a phylogenetically based functional annotation tool (https://github.com/geneontology/paint) , Apollo{\textemdash}a genomic feature editor designed to support structural annotation of gene models (http://genomearchitect.org/),\  and Noctua{\textemdash}a biological-process model builder designed for describing the functional roles of gene products (http://noctua.berkeleybop.org/).

In addition to the graphical elements we summarize the requirements that enable any annotation tool to meet the needs of the research community, including: Real time updates to allow geographically dispersed researchers to conduct joint efforts; Parallel chat mechanisms; Maintaining an evidence trail for all assertions; Well supported history mechanisms and browsing of past versions; Providing different levels of permissions; Facilitating publication; Responsiveness to users{\textquoteright} requests; Documentation and dedicated resources for training and support.

}, keywords = {BIG-data, data visualization, genome, ontology}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Suzanna Lewis} } @conference {IT603, title = {IT603: Improving the Semantics of Drug Prescriptions with a Realist 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 = {

Electronic prescriptions are supported as a means to reduce adverse drug events, but the ambiguities and overspecificities of prescription semantics along with their lack of standardization reduce adoption, limit interoperability and are potential sources of error. Ontologies in the OBO Foundry, founded on realist methodology, have been successful in fostering the logical, scientifically accurate data standards that the domain of drug prescriptions is currently in need of. This paper illustrates some problems regarding the structuration of current electronic prescriptions, and demonstrates how the Prescription of Drugs Ontology (PDRO) addresses these issues with improved semantics founded on OBO and realist principles. PDRO reuses classes and object properties from IAO, OBI, OGMS, OMRSE and DRON, introducing new entities within its scope and proposing entities within those of its imported domains that may be useful to other health care and information artifact-related ontologies in the OBO Foundry. PDRO aims at improving the semantics of drug prescriptions and prospectively enabling the interoperability of prescription data.

}, url = {http://ceur-ws.org/Vol-1747/IT603_ICBO2016.pdf}, author = {Jean-Francois Ethier and Ryeyan Taseen and Luc Lavoie and Adrien Barton} } @conference {IT407, title = {IT407: Annotating germplasm to Planteome reference 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 = {

An expected use case of plant phenotype ontologies will be the identification of germplasm containing particular traits of interest. If phenotype data from experiments is annotated using ontologies, it makes sense to include annotations to that germplasm source. A lack of standardized data formatting reduces the utility of these data. Standardizing germplasm data, including links to germplasm databases, or distribution locations improves collaboration, and benefits both researchers and the scientific community as a whole. All plant traits contained in the Planteome reference ontologies are searchable, and interconnected through relationships in the ontology. All data annotated to these reference ontologies will be displayed, shareable, and computable through the Planteome website (www.planteome.org) and APIs. This manuscript will discuss the advantages of standardizing germplasm trait annotation, and the semi-automated process developed to achieve such standardization.

}, url = {http://ceur-ws.org/Vol-1747/IT407_ICBO2016.pdf}, author = {Austin Meier and Laurel Cooper and Justin Elser and Pankaj Jaiswal and Marie-Ang{\'e}lique Laporte} } @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 {IT204, title = {IT204: A sustainable approach to knowledge representation in the domain of sustainability: bridging SKOS 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 = {n/a}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Chris Mungall and Mark Jensen and Marie-Ang{\'e}lique Laporte and Pier Buttigieg} } @conference {IP28, title = {IP28: uc_FIDO: unambiguous characterization of food interactions with drugs 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 = {

uc_FIDO is an ontology that unambiguously characterizes food interactions with drugs in the human body. This ontology is part of a group of food ontologies describing food and the human experience at the International Center for Food Ontology Operability, Data and Semantics (IC-FOODS) at UC Davis. The first of its kind, uc_FIDO characterizes relations between food, medicine, and human health. uc_FIDO brings together several existing ontologies related to anatomy, metabolic pathways, biological processes, drug ingredients and food structures. Through these ontologies, uc_FIDO annotates relationships between food and drug bioactives, human physiological conditions, and biological reaction pathways. Relationships that link together fully characterize various food interactions with drugs and their effects. The current dearth of ontologies for characterizing foods limits advancement of informatics solutions for improving health. As ontologies of foods are developed, it becomes necessary to describe ingredients, bioactive molecules, potential toxins, and other molecules in food interacting with drugs and the human body.

}, url = {http://ceur-ws.org/Vol-1747/IP28_ICBO2016.pdf}, author = {Constantine Spyrou and Matthew Lange} } @conference {IP22, title = {IP22: SourceData: Making Data discoverable}, 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/BT101_ICBO2016.pdf}, author = {Nancy George and Sara El-Gebali and Thomas Lemberger} } @conference {IP17, title = {IP17: Comparison of ontology mapping techniques to map plant trait 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 = {

Crop specific ontologies for phenotype annotations in breeding have proliferated over the last 10 years. Across-crop data interoperability involves linking those ontologies together. For this purpose, the Planteome project is mapping the Crop Ontology traits (www.cropontology.org) to the reference ontology for plant traits, Trait Ontology (TO). Manual mapping is time-consuming and not sustainable in the long-run as ontologies keep on evolving and multiplicating. We are thus working on developing reliable automated mapping techniques to assist curators in performing semantic integration. Our study shows the benefit of the ontology matching technique based on formal definitions and shared ontology design patterns, compared to standard automatic ontology matching algorithm, such as AML (AgreementMakerLight).

}, url = {http://ceur-ws.org/Vol-1747/IP17_ICBO2016.pdf}, author = {Marie-Ang{\'e}lique Laporte and L{\'e}o Valette and Laurel Cooper and Chris Mungall and Austin Meier and Pankaj Jaiswal and Elizabeth Arnaud} } @conference {IP15, title = {IP15: uc_Milk: An ontology for scientifically-based unambiguous characterization of mammalian milk, their composition and the biological processes giving rise to their creation}, 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 = {

Recent efforts in biological ontology go to great lengths to unambiguously categorize biological entities and phenomena of the natural world, as well as their relationships with each other. This paper illustrates the importance of unambiguously characterizing mammalian milk because milk is a complex mixture of many chemical components and thus represents a key role in infant nourishment and development. In addition to the build of a computable knowledge base around mammalian milk, ontological modeling of this aspect of biology and chemistry enable increased understanding of mammalian milk composition and the biological structures and biochemical processes giving rise to their creation. Utilizing unambiguous vocabularies to compare human milk with other mammalian milks relative to the biological and behavioral survival challenges facing varied mammalian organisms and the phenotypic qualities each milk confers, is a fundamental goal of this project.

}, url = {http://ceur-ws.org/Vol-1747/IP15_ICBO2016.pdf}, author = {Emeline Colet and Matthew Lange} } @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 {IP13, title = {IP13: uc_Eating: Ontology for unambiguous characterization of eating and food habits}, 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 uc_Eating ontology is a standardized unambiguous characterization system for modeling human food habits and eating processes. The uc_Eating ontology along with the physiological, environmental, behavioral, and food ontologies it maps to, provide an infrastructure for annotating the relationships between food, food consumption, eating behaviors, and environments creating a foundation for computable knowledge bases around food and beverage consumption scenarios, their observation, interrogation, and manipulation at biological, behavioral, and environmental levels.

}, url = {http://ceur-ws.org/Vol-1747/IP13_ICBO2016.pdf}, author = {Kimiya Taji and Matthew Lange} } @conference {IP11, title = {IP11: Uc_Sense: An Ontology for Scientifically-based Unambiguous Characterization of Sensory Experiences}, 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 = {

Recent efforts in biological ontology go to great lengths to unambiguously categorize biological entities and phenomena of the natural world, as well as their relationships with each other. This paper illustrates the importance of unambiguously characterizing the perception of entities relative to biological apparati required for specific modes of sensing, physiological conduction of sensed experiences, along with subjective interpretation and communication of sensed experiences. In addition to building a computable knowledge base around existing sensory science, ontological modelling of this aspect of biology will enable an increased understanding about alternate perceptions of identical stimuli. Leveraging this understanding to modulate desired behavior toward increased health and happiness outcomes is a fundamental goal of this project.

}, url = {http://ceur-ws.org/Vol-1747/IP11_ICBO2016.pdf}, author = {Aaron Baer and Matthew Lange} } @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 {D203, title = {D202: Reusing the NCBO BioPortal technology for agronomy to build AgroPortal}, 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 vocabularies and ontologies are produced to represent and annotate agronomic data. By reusing the NCBO BioPortal technology, we have already designed and implemented an advanced prototype ontology repository for the agronomy domain. We plan to turn that prototype into a real service to the community. The AgroPortal project aims at reusing the scientific outcomes and experience of the biomedical domain in the context of plant, agronomic, food, environment (perhaps animal) sciences. We offer an ontology portal which features ontology hosting, search, versioning, visualization, comment, recommendation, enables semantic annotation, as well as storing and exploiting ontology alignments. All of these within a fully semantic web compliant infrastructure. The AgroPortal specifically pays attention to respect the requirements of the agronomic community in terms of ontology formats (e.g., SKOS, trait dictionaries) or supported features. In this paper, we present our prototype as well as preliminary outputs of four driving agronomic use cases. With the experience acquired in the biomedical domain and building atop of an already existing technology, we think that AgroPortal offers a robust and stable reference repository that will become highly valuable for the agronomic domain.

}, url = {http://ceur-ws.org/Vol-1747/D202_ICBO2016.pdf}, author = {Clement Jonquet and Anne Toulet and Elizabeth Arnaud and Sophie Aubin and ED Yeumo and Vincent Emonet and John Graybeal and Mark A. Musen and Cyril Pommier and Pierre Larmande} } @conference {D101-BP04, title = {D106-BP04: Enhancing Information Accessibility of Publications with Text Mining and 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 = {

We present an ongoing effort on utilizing text mining methods and existing biological ontologies to help readers to access the information contained in the scientific articles. Our approach includes using multiple strategies for biological entity detection and using association analysis on extracted analysis. The entity extraction processes utilizes regular expression rules, ontologies, and keyword dictionary to get a comprehensive list of biological entities. In addition to extract list of entities, we also apply natural language processing and association analysis techniques to generate inferences among entities and comparing to known relations documented in the existing ontologies.

}, url = {http://ceur-ws.org/Vol-1747/D106-BP04_ICBO2016.pdf}, author = {Weijia Xu and Amit Gupta and Pankaj Jaiswal and Crispin Taylor and Patti Lockhart} } @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 {BT204, title = {BT204: CancerMine: Knowledge base construction for personalised cancer treatment}, 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 = {

Knowledge of the relevant genomic aberrations that drive a particular cancer type is necessary to accelerate efficient interpretation of genomic data and enable large-scale endeavors in precision medicine. Currently, this field is limited by the lack of focused and scalable literature curation tools that can reliably capture the required information. Here we present a knowledge-base of genes that have been described in the literature as drivers, oncogenes or tumour suppressors with respect to a specific type of cancer. We have annotated a large body of literature which reports oncogenic aberrations using a custom designed annotation tool. We then applied VERSE, an in-house relation extraction tool, to catalogue driver mutations and illustrate the ability to build a useful resource for clinical interpretation of genomic data for personalized treatment approaches.

}, url = {http://ceur-ws.org/Vol-1747/BT204_ICBO2016.pdf}, author = {Jake Lever and Martin Jones and Steven Jm Jones} } @conference {BT203, title = {BT203: MutD {\textendash} A PubMed Scale Resource for Protein Mutation-Disease Relations through Bio-Medical Literature Mining}, 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 amount of information about the role of gene variants and mutations in diseases is available in curated databases such as OMIM, ClinVar, and UniprotKB. However, much of this information remains {\textquoteleft}locked{\textquoteright} in the unstructured form in the scientific publications. Since manual curation involves significant human effort and time there is always a lag in the information between the curated databases and the literature. The recent findings published in the literature takes significant time to find its way into the curated knowledgebase. Text mining approaches can accelerate the process of assembling this knowledge from the published literature. However, developing a text-mining system with semantic understanding capability in the biomedical domain is very challenging. In an earlier work, we described MutD, a literature mining system that extracts relationship between protein point mutation and diseases from bio-medical abstracts. In this abstract, we present access to a PubMed scale resource through a web interface that allows users to retrieve protein point mutation-disease relations extracted through biomedical literature mining.

}, url = {http://ceur-ws.org/Vol-1747/BT203_ICBO2016.pdf}, author = {RK Elayavilli and Majid Rastegar-Mojarad and Hongfang Liu} } @conference {BT103, title = {BT104: Crowdsourcing Protein Family Database 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 propose a novel method for crowdsourcing a protein family database. We discuss how we intend to identify novel groupings of proteins from user sequence similarity search, and how text mining will be applied to assist in annotation of these novel groupings, and more broadly as an enrichment of protein sequence similarity search results.

}, url = {http://ceur-ws.org/Vol-1747/BT104_ICBO2016.pdf}, author = {Matt Jeffryes and Maria Liakata and Alex Bateman} } @conference {342, title = {BT101: SourceData: Making Data discoverable}, 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 = {

In molecular and cell biology, most of the data presented in published papers are not available in formats that allow for direct analysis and systematic mining. The goal of the SourceData project (http://sourcedata.embo.org) is to make published data easier to find, to connect papers containing related information and to promote the reuse and novel analysis of published data. The main concept underlying the project is that the structure of a dataset provides information about the design of the study in question and can be exploited in powerful data-oriented search strategies. SourceData has therefore developed tools to generate machine-readable descriptive metadata from figures in published manuscripts. Experimentally tested hypotheses are represented as directed relationships between standardized biological entities. Once processed, a comprehensive {\textquoteleft}scientific knowledge graph{\textquoteright} can be generated from this data (see demo video1 at https://vimeo.com/sourcedata/kg), making the body of data efficiently searchable. Importantly, this graph is objectively grounded in published data and not on the potentially subjective interpretation of the results.\ 

}, url = {http://ceur-ws.org/Vol-1747/BT101_ICBO2016.pdf}, author = {Nancy George and Sara El-Gebali and Thomas Lemberger} } @conference {BIT105, title = {BIT105: A Web Application for Extracting Key Domain Information for Scientific Publications using 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 = {

We present demos of an ongoing project, domain informational vocabulary extraction (DIVE), which aims to enrich digital publications through entity and key informational words detection and by adding additional annotations. The system implements multiple strategies for biological entity detection, including using regular expression rules, ontologies, and a keyword dictionary. These extracted entities are then stored in a database and made accessible through an interactive web application for curation and evaluation by authors. Through the web interface, the user can make additional annotations and corrections to the current results. The updates can then be used to improve the entity detection in subsequent processed articles. Although the system is being developed in the context of annotating journal articles, it can be also be beneficial to domain curators and researchers at large.

}, url = {http://ceur-ws.org/Vol-1747/BIT105_ICBO2016.pdf}, author = {Weijia Xu and Amit Gupta and Pankaj Jaiswal and Crispin Taylor and Patti Lockhart} } @conference {BIT104, title = {BIT104: Cardiovascular Health and Physical Activity: A Model for Health Promotion and Decision Support 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 = {

Current cardiovascular disease decision support systems (DSS) rely primarily on ontologies that characterize and quantify disease, recommending appropriate pharmacotherapy (PT) and/or surgical interventions (SI). PubMed and Google Scholar searches reveal no specific ontologies or literature related to DSS for recommending physical activity (PA) and diet interventions (DI) for cardiovascular health and fitness (CVHF) improvement. This dearth of CVHF-PA/DI structured knowledge repositories has resulted in a scarcity of user-friendly tools for scientifically validated information retrieval about CVHF improvement. Advancement of health science depends on timely development and implementation of health (rather than disease) ontologies. We developed a time-efficient workflow for constructing/maintaining structured knowledge repositories capable of providing informational underpinnings for CVHF- PA/DI ontologies and DSS that support health promotion, including precise, personalized exercise prescription. This workflow creates conceptual lattices about effects of varied PA on CVHF. These conceptual maps lay the foundation for accelerated creation of health-focused ontologies, which ultimately equip DSS with CVHF knowledge related PA and DI.

}, url = {http://ceur-ws.org/Vol-1747/BIT104_ICBO2016.pdf}, author = {Vimala Ponna and Aaron Baer and Matthew Lange} } @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} } @conference {BIT101, title = {BIT101-D204: Large-scale Semantic Indexing with Biomedical 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 = {

We introduce PubTator, a web-based application that enables large-scale semantic indexing and automatic concept recognition in biomedical ontologies. Not only was PubTator formally evaluated and top-rated in BioCreative, it also has been widely adopted and used by the scientific community from around the world, supporting both research projects and real-world applications in biocuration, crowdsourcing and translational bioinformatics.

}, url = {http://ceur-ws.org/Vol-1747/BIT101-D204_ICBO2016.pdf}, author = {Chih-Hsuan Wei and Robert Leaman and Zhiyong Lu} }