Metarel: an Ontology to support the inferencing of Semantic Web relations within Biomedical Ontologies

While OWL, the Web Ontology Language, is often regarded as the preferred language for Knowledge Representation in the world of the Semantic Web, the potential of direct representation in RDF, the Resource Description Framework, is underestimated. Here we show how ontologies adequately represented in RDF could be semantically enriched with SPARUL. To deal with the semantics of relations we created Metarel, a meta-ontology for relations. The utility of the approach is demonstrated by an application on Gene Ontology Annotation (GOA) RDF graphs in the RDF Knowledge Base BioGateway. We show that Metarel can facilitate inferencing in BioGateway, which allows for queries that are otherwise not possible. Metarel is available on http://www.metarel.org

Validating clusterings of gene expression data

We propose a measure for the validation of clusterings of gene expression data. This measure also useful to estimate missing gene expression levels, based the similarity information contained in a given clustering. It is shown that this measure is an improvement over the figure of merit, an existing validation measure especially developed for clusterings of gene expression data

Scientific knowledge in the age of computation: explicated, computable and manageable?

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management (KM):second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli (2014, 2016), Efstathiou (2012, 2016) and Hislop (2013) in our analysis of the use of KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge.; Con el aumento de la publicación y la producción de datos, el conocimiento científico no solo es reconocido como un logro, sino también como un desafío. Las publicaciones y los datos científicos se tratan cada vez más como recursos que deben ser ‘gestionados’ digitalmente. Esto da lugar a la Gestión del Conocimiento científico (Knowledge Management (KM)): labor científica de segundo orden destinada a recopilar, cuidar y movilizar de forma directa el conocimiento disciplinario de primera mano y los datos para proporcionar nuevos conocimientos científicos de primer orden. Seguimos el trabajo de Leonelli (2014, 2016), Efstathiou (2012, 2016) y Hislop (2013) en nuestro análisis del uso de la KM en la biología de sistemas semánticos. A través de una descripción filosófica empírica de la investigación biológica habilitada para KM, argumentamos que KM ayuda a producir un nuevo conocimiento biológico de primer orden que no existía antes y que no podría haber sido producido por medios tradicionales. El trabajo de KM está facultado para concebir el “conocimiento” como un objeto para la ciencia computacional: como algo explicitado en el texto de artículos biológicos y como computable a través de datos y metadatos apropiados. Sin embargo, los conceptos fundados permiten el riesgo computacional de KM de centrarse solo en los datos que se pueden tratar de manera computacional como conocimiento, subestimando el conocimiento basado en la práctica y su importancia para garantizar la validez del conocimiento “manejable” como conocimiento

Scientific knowledge in the age of computation

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management : second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli, Efstathiou and Hislop in our analysis of the use of KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge

ExTRI: Extraction of transcription regulation interactions from literature

The regulation of gene transcription by transcription factors is a fundamental biological process, yet the relations between transcription factors (TF) and their target genes (TG) are still only sparsely covered in databases. Text-mining tools can offer broad and complementary solutions to help locate and extract mentions of these biological relationships in articles. We have generated ExTRI, a knowledge graph of TF-TG relationships, by applying a high recall text-mining pipeline to MedLine abstracts identifying over 100,000 candidate sentences with TF-TG relations. Validation procedures indicated that about half of the candidate sentences contain true TF-TG relationships. Post-processing identified 53,000 high confidence sentences containing TF-TG relationships, with a cross-validation F1-score close to 75%. The resulting collection of TF-TG relationships covers 80% of the relations annotated in existing databases. It adds 11,000 other potential interactions, including relationships for ~100 TFs currently not in public TF-TG relation databases. The high confidence abstract sentences contribute 25,000 literature references not available from other resources and offer a wealth of direct pointers to functional aspects of the TF-TG interactions. Our compiled resource encompassing ExTRI together with publicly available resources delivers literature-derived TF-TG interactions for more than 900 of the 1500–1600 proteins considered to function as specific DNA binding TFs. The obtained result can be used by curators, for network analysis and modelling, for causal reasoning or knowledge graph mining approaches, or serve to benchmark text mining strategies.We thank the participants of the COST Action GREEKC (CA15205) for fruitful discussions during workshops supported by COST (European Cooperation in Science and Technology).Peer ReviewedPostprint (published version

EU Agricultural Trade Relations with Asian Countries

This report investigates the possible effects of a Free Trade Area between the European Union and its three main trading partners: India, South Korea and the Association of South East Asian Nations (ASEAN) countries, focusing on the agricultural sector. The report includes an analysis of the ongoing bilateral negotiations and bilateral trade flows based on trade policy (at tariff line level), comparative advantages assessment and a modeling analysis of the implications of preferential liberalization in both, a CGE (LEITAP, modified version of the GTAP model) and a partial equilibrium context (PEATSim model). Results show that the overall level of agri-food production in Asian countries is driven by income and population growth, main determinants of increase in demand particularly in India. Different degrees of liberalization in bilateral agricultural and food trade do not significantly affect the total amount of agricultural production in Asian countries and the EU, however, it leads to trade creation and trade diversion effects. Bilateral trade between EU and the Asian countries tend to increase (trade creation) whilst Asian exports to third countries tend to diminish (trade diversion). The implementation of the different policy options (partial and full liberalization) determines a decline in EU overall imports due to the prevailing effect of trade diversion over trade creation. ASEAN imports and export from/to the EU grow considerably under the liberalization scenarios determining a positive net trade of 22 billion ¿ euro for the agri-food sector. Under full liberalization scenario Indian agri-food exports to the EU grow by 4 billion ¿ reaching almost 6.3 billion ¿. Indian agri-food imports grow even faster from 0.2 up to 19 billion ¿. The value of South Korean agri-food exports to the EU grows from 46 million ¿ in the baseline to 4.9 billion ¿ under the full liberalization. Total European agri-food exports expand by almost 11% from partial to the full liberalisation scenario.JRC.DG.J.5-Agriculture and Life Sciences in the Econom

Constraining the nuclear equation of state at subsaturation densities

Only one third of the nucleons in $^{208}$Pb occupy the saturation density area. Consequently nuclear observables related to average properties of nuclei, such as masses or radii, constrain the equation of state (EOS) not at saturation density but rather around the so-called crossing density, localised close to the mean value of the density of nuclei: $\rho\simeq$0.11 fm$^{-3}$. This provides an explanation for the empirical fact that several EOS quantities calculated with various functionals cross at a density significantly lower than the saturation one. The third derivative M of the energy at the crossing density is constrained by the giant monopole resonance (GMR) measurements in an isotopic chain rather than the incompressibility at saturation density. The GMR measurements provide M=1110 $\pm$ 70 MeV (6% uncertainty), whose extrapolation gives K$_\infty$=230 $\pm$ 40 MeV (17% uncertainty).Comment: 4 pages, 4 figure