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

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

The improvement and more and more common deployment of high-throughput experimental equipment within the existence sciences is giving upward push to various huge, c- plex and priceless facts assets. This origin of experimental info und- pins the systematic examine of organismsand illnesses, which increasinglydepends at the improvement of versions of organic platforms. the improvement of those types usually calls for integration of various experimental info assets; as soon as built, the versions themselves develop into information and current new integration demanding situations for projects similar to interpretation, validation and comparability. the knowledge Integration within the existence Sciences (DILS) convention sequence brings jointly information and data administration researchers from the pc s- ence study group with bioinformaticians and computational biologists, to enhance the certainty of ways rising info integration thoughts can tackle requisites identi?ed within the lifestyles sciences. DILS 2010 was once the 7th occasion within the sequence and was once held in Goth- burg, Sweden in the course of August 25–27, 2010. The linked complaints comprise 14 peer-reviewed papers and a couple of invited papers. The periods addressed ontology engineering, and specifically, evolution, matching and debugging of ontologies, akeycomponentforsemanticintegration;Web servicesasanimportanttechn- ogy for information integration within the existence sciences; facts and textual content mining ideas for locating and spotting biomedical entities and relationships among those entities; and knowledge administration, introducing info integration strategies for di?erent forms of purposes concerning melanoma, platforms biology and - croarray experimental information, and an procedure for integrating ranked facts within the lifestyles sciences.

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Example text

Note that this ontology region is utilized to collect all ontology concepts that have been retired. Other regions of high interest concern “Drugs and Chemicals” topics such as “Industrial Aid” or “Natural Product”. 4 Tracking the Stability of Ontology Regions A sample application of our discovery algorithm is tracking the stability of ontology regions over time. Particularly, we apply our region measures for different time periods to determine the change intensity of different regions over time.

We can thus observe certain trends in the evolution of ontologies that are of interest to ontology users. 32 M. Hartung et al. Fig. 6. Tracking of avg_costs for sample regions in NCIT (2004-2009) As an example we applied region tracking on NCIT between 2004 and 2009 for its 20 main categories. The computation uses a sliding window in the following way. , for each window we compute region measures for the selected categories and consider them for a final trend analysis. , to find out where and when massive development took place or not.

Note that this ontology region is utilized to collect all ontology concepts that have been retired. Other regions of high interest concern “Drugs and Chemicals” topics such as “Industrial Aid” or “Natural Product”. 4 Tracking the Stability of Ontology Regions A sample application of our discovery algorithm is tracking the stability of ontology regions over time. Particularly, we apply our region measures for different time periods to determine the change intensity of different regions over time.

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