Polygenic trait analysis by neural network learning.
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.
Fu
L
L
University of Florida, Department of Computer and Information Sciences, Gainesville 32611.
D001185:Artificial Intelligence; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D005796:Genes; D030342:Genetic Diseases, Inborn / Q000235:genetics; D008040:Genetic Linkage / Q000235:genetics*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated