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Recently, the world is hunting for using life sciences data in solving the problems of fighting hunger in the next coming years. Integrating this data can be useful in areas of agricultural bioinformatics and other disciplines. However, efficient integration techniques must be developed to biological data since biological data has its own challenging characteristics, such as the existence of huge...
We developed a novel analytical environment to aid in the examination of the extensive amount of interconnected data available for genome projects. Our focus is to enable flexibility and abstraction from implementation details, while retaining the expressivity required for post-genomic research. To achieve this goal, we associated genomics data to ontologies and implemented a query formulation and...
This paper reports on our experience in modeling and employing ontology-inferred knowledge to support and improve data mining tasks of yeast protein interactions for knowledge discovery. This objective has been accomplished by providing simplified access to units of intersecting proteome data and information from different biological databases and bio-ontologies, and utilizing a logical framework...
Provenance information in eScience is metadata that's critical to effectively manage the exponentially increasing volumes of scientific data from industrial-scale experiment protocols. Semantic provenance, based on domain-specific provenance ontologies, lets software applications unambiguously interpret data in the correct context. The semantic provenance framework for eScience data comprises expressive...
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