Abstract
In this paper we present a novel knowledge-based approach that aims at helping scientists to face and resolve a large number of proteomics problem. The system architecture is based on an ontology to model the knowledge base, a reasoner that starting from the user’s request and a set of rules builds the workflow of tasks to be done, and an executor that runs the algorithms and software scheduled by the reasoner. The system can interact with the user showing him intermediate results and several options in order to refine the workflow and supporting him to choose among different forks. Thanks to the presence of the knowledge base and the modularity provided by the ontology, the system can be enriched with new expertise in order to deal with other proteomic or bioinformatics issues. Two possible application scenarios are presented.
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Fiannaca, A., Gaglio, S., La Rosa, M., Peri, D., Rizzo, R., Urso, A. (2010). A Proposed Knowledge Based Approach for Solving Proteomics Issues. In: Masulli, F., Peterson, L.E., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2009. Lecture Notes in Computer Science(), vol 6160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14571-1_23
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DOI: https://doi.org/10.1007/978-3-642-14571-1_23
Publisher Name: Springer, Berlin, Heidelberg
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