Selected papers from the 16th Annual Bio-Ontologies Special Interest Group Meeting

Copyright @ 2014 Soldatova et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Over the 16 years, the Bio-Ontologies SIG at ISMB has provided a forum for vibrant discussions of the latest and most innovative advances in the research area of bio-ontologies, its applications to biomedicine and more generally in the organisation, sharing and re-use of knowledge in biomedicine and the life sciences. The six papers selected for this supplement span a wide range of topics including: ontology-based data integration, ontology-based annotation of scientific literature, ontology and data model development, representation of scientific results and gene candidate prediction

Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 201

A linked data representation for summary statistics and grouping criteria

Summary statistics are fundamental to data science, and are the buidling blocks of statistical reasoning. Most of the data and statistics made available on government web sites are aggregate, however, until now, we have not had a suitable linked data representation available. We propose a way to express summary statistics across aggregate groups as linked data using Web Ontology Language (OWL) Class based sets, where members of the set contribute to the overall aggregate value. Additionally, many clinical studies in the biomedical field rely on demographic summaries of their study cohorts and the patients assigned to each arm. While most data query languages, including SPARQL, allow for computation of summary statistics, they do not provide a way to integrate those values back into the RDF graphs they were computed from. We represent this knowledge, that would otherwise be lost, through the use of OWL 2 punning semantics, the expression of aggregate grouping criteria as OWL classes with variables, and constructs from the Semanticscience Integrated Ontology (SIO), and the World Wide Web Consortium’s provenance ontology, PROV-O, providing interoperable representations that are well supported across the web of Linked Data. We evaluate these semantics using a Resource Description Framework (RDF) representation of patient case information from the Genomic Data Commons, a data portal from the National Cancer Institute

Co-Designing an organic framework: the ”REF’AB” Project in France

To help the development of Organic Food and Farming (OFF) systems, there is the need to consolidate conversions to OF, to support entry into organic farming, and to develop existing organic farms. Therefore a group of extensionists, researchers and educators has being working 2 years long to co-design a framework for OFF, taking into account the need to (i) embrace all forms of OFF, and at the same time to (ii) address the specificities of OFF. This project, called REF’AB (REFerentiel de l’Agriculture Biologique, 2010-2012) aims at designing the organization of set of references produced on shared methodological bases. The approach consisted in identifying key-issues of OFF systems, defining indicators, identifying relevant databases and references to feed the framework, and providing recommendations for governance to optimise the establishment and circulation of references. The outcomes of this project is that first it was not necessarily clear what a framework for OFF should be, and therefore a conference of consensus was used as a methodological tool to share views by multi-stakeholders. Second, we used sustainability assessment as an integrative process of the three levels (technical-economic, environmental, and social) and third, to manage to share a more transversal approach, we proposed a framework with 3 levels of key-proprieties: 1) security and efficiency, 2) durability through the protection of resources, and 3) autonomy and resilience

Power grip, pinch grip, manual muscle testing or thenar atrophy - which should be assessed as a motor outcome after carpal tunnel decompression? A systematic review

<p>Abstract</p> <p>Background</p> <p>Objective assessment of motor function is frequently used to evaluate outcome after surgical treatment of carpal tunnel syndrome (CTS). However a range of outcome measures are used and there appears to be no consensus on which measure of motor function effectively captures change. The purpose of this systematic review was to identify the methods used to assess motor function in randomized controlled trials of surgical interventions for CTS. A secondary aim was to evaluate which instruments reflect clinical change and are psychometrically robust.</p> <p>Methods</p> <p>The bibliographic databases Medline, AMED and CINAHL were searched for randomized controlled trials of surgical interventions for CTS. Data on instruments used, methods of assessment and results of tests of motor function was extracted by two independent reviewers.</p> <p>Results</p> <p>Twenty-two studies were retrieved which included performance based assessments of motor function. Nineteen studies assessed power grip dynamometry, fourteen studies used both power and pinch grip dynamometry, eight used manual muscle testing and five assessed the presence or absence of thenar atrophy. Several studies used multiple tests of motor function. Two studies included both power and pinch strength and reported descriptive statistics enabling calculation of effect sizes to compare the relative responsiveness of grip and pinch strength within study samples. The study findings suggest that tip pinch is more responsive than lateral pinch or power grip up to 12 weeks following surgery for CTS.</p> <p>Conclusion</p> <p>Although used most frequently and known to be reliable, power and key pinch dynamometry are not the most valid or responsive tools for assessing motor outcome up to 12 weeks following surgery for CTS. Tip pinch dynamometry more specifically targets the thenar musculature and appears to be more responsive. Manual muscle testing, which in theory is most specific to the thenar musculature, may be more sensitive if assessed using a hand held dynamometer – the Rotterdam Intrinsic Handheld Myometer. However further research is needed to evaluate its reliability and responsiveness and establish the most efficient and psychometrically robust method of evaluating motor function following surgery for CTS.</p

Adding a Little Reality to Building Ontologies for Biology

BACKGROUND: Many areas of biology are open to mathematical and computational modelling. The application of discrete, logical formalisms defines the field of biomedical ontologies. Ontologies have been put to many uses in bioinformatics. The most widespread is for description of entities about which data have been collected, allowing integration and analysis across multiple resources. There are now over 60 ontologies in active use, increasingly developed as large, international collaborations. There are, however, many opinions on how ontologies should be authored; that is, what is appropriate for representation. Recently, a common opinion has been the "realist" approach that places restrictions upon the style of modelling considered to be appropriate. METHODOLOGY/PRINCIPAL FINDINGS: Here, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models. CONCLUSIONS/SIGNIFICANCE: From our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology

Semantic Web integration of Cheminformatics resources with the SADI framework

<p>Abstract</p> <p>Background</p> <p>The diversity and the largely independent nature of chemical research efforts over the past half century are, most likely, the major contributors to the current poor state of chemical computational resource and database interoperability. While open software for chemical format interconversion and database entry cross-linking have partially addressed database interoperability, computational resource integration is hindered by the great diversity of software interfaces, languages, access methods, and platforms, among others. This has, in turn, translated into limited reproducibility of computational experiments and the need for application-specific computational workflow construction and semi-automated enactment by human experts, especially where emerging interdisciplinary fields, such as systems chemistry, are pursued. Fortunately, the advent of the Semantic Web, and the very recent introduction of RESTful Semantic Web Services (SWS) may present an opportunity to integrate all of the existing computational and database resources in chemistry into a machine-understandable, unified system that draws on the entirety of the Semantic Web.</p> <p>Results</p> <p>We have created a prototype framework of Semantic Automated Discovery and Integration (SADI) framework SWS that exposes the QSAR descriptor functionality of the Chemistry Development Kit. Since each of these services has formal ontology-defined input and output classes, and each service consumes and produces RDF graphs, clients can automatically reason about the services and available reference information necessary to complete a given overall computational task specified through a simple SPARQL query. We demonstrate this capability by carrying out QSAR analysis backed by a simple formal ontology to determine whether a given molecule is drug-like. Further, we discuss parameter-based control over the execution of SADI SWS. Finally, we demonstrate the value of computational resource envelopment as SADI services through service reuse and ease of integration of computational functionality into formal ontologies.</p> <p>Conclusions</p> <p>The work we present here may trigger a major paradigm shift in the distribution of computational resources in chemistry. We conclude that envelopment of chemical computational resources as SADI SWS facilitates interdisciplinary research by enabling the definition of computational problems in terms of ontologies and formal logical statements instead of cumbersome and application-specific tasks and workflows.</p

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd