A theory of diagnosis from first principles
Artificial Intelligence
A logical framework for default reasoning
Artificial Intelligence
Abduction to plausible causes: an event-based model of belief update
Artificial Intelligence
Computational intelligence: a logical approach
Computational intelligence: a logical approach
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
HinCyc: A Knowledge Base of the Complete Genome and Metabolic Pathways of H. influenzae
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Abduction in Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data
Journal of Biomedical Informatics
A-system: problem solving through abduction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
GenePath: a system for inference of genetic networks and proposal of genetic experiments
Artificial Intelligence in Medicine
Applications of action languages in cognitive robotics
Correct Reasoning
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The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. These revision or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding “pattern” in data and leave the reasoning to biologists. Few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalism they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with incomplete knowledge, which is often the case with respect to biochemical networks. We present a knowledge based framework for the general problem of hypothesis formation. The framework has been implemented by extending BioSigNet-RR. BioSigNet-RR is a knowledge based system that supports elaboration tolerant representation and non-monotonic reasoning. The main features of the extended system include: (1) seamless integration of hypothesis formation with knowledge representation and reasoning; (2) use of various resources of biological data as well as human expertise to intelligently generate hypotheses. The extended system can be considered as a prototype of an intelligent research assistant of molecular biologists. The system is available at http://www.biosignet.org.