CEUR workshop proceedings最新文献

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.

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.

We describe an approach for performing qualitative, systems-level causal analyses on biosimulation models that leverages semantics-based modeling formats, formal ontology, and automated inference. The approach allows users to quickly investigate how a qualitative perturbation to an element within a model's network (an increment or decrement) propagates throughout the modeled system. To support such analyses, we must interpret and annotate the semantics of the models, including both the physical properties modeled and the dependencies that relate them. We build from prior work understanding the semantics of biological properties, but here, we focus on the semantics for dependencies, which provide the critical knowledge necessary for causal analysis of biosimulation models. We describe augmentations to the Ontology of Physics for Biology, via OWL axioms and SWRL rules, and demonstrate that a reasoner can then infer how an annotated model's physical properties influence each other in a qualitative sense. Our goal is to provide researchers with a tool that helps bring the systems-level network dynamics of biosimulation models into perspective, thus facilitating model development, testing, and application.

Organizational structures of healthcare organizations has increasingly become a focus of medical research. In the CAFÉ project we aim to provide a web-service enabling ontology-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 challenge. Evaluation and comparison of these organizations is essential. However, this research challenge is frequently compounded by the lack of a shared terminology and the lack of effective information technology solutions for assessing and comparing these organizations. In this paper we present the Ontology of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological foundation to CAFÉ'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.

We investigate the current extent of term reuse and overlap among biomedical ontologies. We use the corpus of biomedical ontologies stored in the BioPortal repository, and analyze three types of reuse constructs: (a) explicit term reuse, (b) xref reuse, and (c) Concept Unique Identifier (CUI) reuse. While there is a term label similarity of approximately 14.4% of the total terms, we observed that most ontologies reuse considerably fewer than 5% of their terms from a concise set of a few core ontologies. We developed an interactive visualization to explore reuse dependencies among biomedical ontologies. Moreover, we identified a set of patterns that indicate ontology developers did intend to reuse terms from other ontologies, but they were using different and sometimes incorrect representations. Our results suggest the value of semi-automated tools that augment term reuse in the ontology engineering process through personalized recommendations.

United States and European safety standards have evolved to protect workers near Automatic Guided Vehicles (AGV's). However, performance standards for AGV's and mobile robots have only recently begun development. Lessons can be learned from research and standards efforts for mobile robots applied to emergency response and military applications. Research challenges, tests and evaluations, and programs to develop higher intelligence levels for vehicles can also used to guide industrial AGV developments towards more adaptable and intelligent systems. These other efforts also provide useful standards development criteria for AGV performance test methods. Current standards areas being considered for AGVs are for docking, navigation, obstacle avoidance, and the ground truth systems that measure performance. This paper provides a look to the future with standards developments in both the performance of vehicles and the dynamic perception systems that measure intelligent vehicle performance.

Inadequate representation of evidence and knowledge about potential drug-drug interactions is a major factor underlying disagreements among sources of drug information that are used by clinicians. In this paper we describe the initial steps toward developing a foundational domain representation that allows tracing the evidence underlying potential drug-drug interaction knowledge. The new representation includes biological and biomedical entities represented in existing ontologies and terminologies to foster integration of data from relevant fields such as physiology, anatomy, and laboratory sciences.

A massive effort is underway to map the structure of the Drosophila nervous system and to genetically dissect its function. Virtual Fly Brain (VFB; http://www.virtualflybrain.org) is a popular, OWL-based resource providing neuroinformatics support for this work. It provides: curated descriptions of brain regions and neurons; queries for neurons based on their relationship to gross neuroanatomy; and queries for reagents based on their expression patterns. Query results are enriched by OWL axiomatisation allowing basic mereological reasoning. To keep reasoning fast and scalable, VFB confines expressiveness to the EL profile of OWL. As a result, VFB does not provide queries involving negation, despite there being both demand and sufficient information to support them. Recent developments in reasoning technology may make more expressive queries practical. Here we present design patterns to support queries with negation that are compatible with the mereological reasoning used in VFB.