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Data Integration in the Life Sciences: 7th International by Juliana Freire (auth.), Patrick Lambrix, Graham Kemp (eds.)

By Juliana Freire (auth.), Patrick Lambrix, Graham Kemp (eds.)

The improvement and more and more frequent deployment of high-throughput experimental equipment within the existence sciences is giving upward push to varied huge, c- plex and important info assets. This starting place of experimental facts und- pins the systematic learn of organismsand ailments, which increasinglydepends at the improvement of versions of organic platforms. the advance of those versions frequently calls for integration of various experimental info assets; as soon as developed, the versions themselves develop into info and current new integration demanding situations for projects equivalent to interpretation, validation and comparability. the information Integration within the existence Sciences (DILS) convention sequence brings jointly information and information administration researchers from the pc s- ence examine neighborhood with bioinformaticians and computational biologists, to enhance the certainty of the way rising info integration ideas can tackle standards identi?ed within the lifestyles sciences. DILS 2010 was once the 7th occasion within the sequence and used to be held in Goth- burg, Sweden in the course of August 25–27, 2010. The linked court cases include 14 peer-reviewed papers and a couple of invited papers. The periods addressed ontology engineering, and particularly, evolution, matching and debugging of ontologies, akeycomponentforsemanticintegration;Web servicesasanimportanttechn- ogy for info integration within the lifestyles sciences; info and textual content mining strategies for locating and spotting biomedical entities and relationships among those entities; and data administration, introducing information integration strategies for di?erent sorts of purposes concerning melanoma, structures biology and - croarray experimental facts, and an strategy for integrating ranked information within the existence sciences.

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Extra resources for Data Integration in the Life Sciences: 7th International Conference, DILS 2010, Gothenburg, Sweden, August 25-27, 2010. Proceedings

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Analyzing the Evolution of Life Science Ontologies and Mappings. , Froidevaux, C. ) DILS 2008. LNCS (LNBI), vol. 5109, pp. 11–27. Springer, Heidelberg (2008) 10. : Ensembl 2009. Nucleic Acids Research 37(Database issue), D690–D697 (2009) 11. : Efficient Management of Biomedical Ontology Versions. S. ) OTM 2009 Workshops. LNCS, vol. 5872, pp. 574–583. Springer, Heidelberg (2009) 34 M. Hartung et al. 12. : Ontology versioning on the Semantic Web. In: Proceedings of the International Semantic Web Working Symposium (SWWS), pp.

Furthermore, we can combine both kinds of parallelism. In the following, we discuss these parallelization strategies in more detail. 2) we focus on the parallel similarity evaluation of the Cartesian product of concept pairs according to a partitioning of the input ontologies. In particular we will describe how we can parallelize element-level, structure-level and instance-based matchers. 1 Inter-matcher Parallelization Inter-matcher parallelization enables the parallel execution of independently executable matchers to utilize multiple processors for faster match processing.

The idea of the combined algorithm is the following. Having n released ontology versions (O1, …, On) we iterate over all releases and apply computeAggregatedCosts on each pair (Oi,Oi+1). Thus, we cover all version changes between succeeding ontology versions and transfer costs from older ontology versions to the latest ontology version On where the region discovery is applied (computeRegionMeasures). The algorithm findRegions for n ontology versions looks as follows: Algorithm 5: findRegions(ontology versions O1 … On, change costs σ) for all succeeding ontology versions Oi – Oi+1 do Oi+1 := computeAggregatedCosts(Oi, Oi+1, σ) end for computeRegionMeasures(On) 4 Evaluation We evaluated the proposed region discovery algorithm for the well-known Gene Ontology (GO) and the National Cancer Institute Thesaurus (NCIT).

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