Richard E. Neapolitan

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BACKGROUND Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor(More)
Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis. Combinatorial methods for detecting epistasis investigate a subset of combinations of genes(More)
BACKGROUND The interaction between loci to affect phenotype is called epistasis. It is strict epistasis if no proper subset of the interacting loci exhibits a marginal effect. For many diseases, it is likely that unknown epistatic interactions affect disease susceptibility. A difficulty when mining epistatic interactions from high-dimensional datasets(More)
In recent years the belief network has been used increasingly to model systems in AI that must perf orm uncertain inf erence. The de­ velopment of efficient algorithms fo r proba­ bilistic inf erence in belief networks has been a fo cus of much research in AI. Efficient al­ gorithms fo r certain classes of belief networks have been developed, but the(More)