Using JessTab to Integrate Protégé and Jess
IEEE Intelligent Systems
KON^3: A Clinical Decision Support System, in Oncology Environment, Based on Knowledge Management
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
An expert system to predict protein thermostability using decision tree
Expert Systems with Applications: An International Journal
Predicting types of protein-protein interactions using a multiple-instance learning model
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Bayesian network multi-classifiers for protein secondary structure prediction
Artificial Intelligence in Medicine
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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.