Communications of the ACM
Artificial intelligence and molecular biology
AI Magazine - Reports from three of the 1990 Spring symposia and eight workshops held over the past two years
Discovery and representation of causal relationships from a large time-oriented clinical database: the rx project
Refinement of approximate domain theories by knowledge-based neural networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Rule learning by searching on adapted nets
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
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AI techniques have been applied to the domain of DNA sequence analysis in predicting or identifying certain specialized regions, in recognizing genes, and in understanding the evolutionary relationships between sequences. This paper focuses on a kind of genetic pattern recognition, namely, the problem of identifying the gene combinations (patterns) causally related to a given trait determined by multiple genes (a so-called polygenic trait). A novel approach is presented which combines neural-network and knowledge-based techniques. The neural network is trained to predict the trait and then the knowledge embedded in the network is decoded into symbolic patterns. This hybrid approach is evaluated in the domain of identifying genes of insulin dependent diabetes mellitus. The consistency between the results with this approach and those reported in genetic literature supports the viability of this approach.