Epistasis (gene-gene interaction) is crucial to predicting genetic disease. Our work tackles the computational challenges faced by previous works in epistasis detection by modeling it as a one-step Markov Decision Process where the state is genome data, the actions are the interacted genes, and the reward is an interac…
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SAERMA combines deep learning and rule mining to identify SNP interactions.
problem Identifying significant SNPs and their interactions in complex diseases.
method Stacked Autoencoder Rule Mining Algorithm (SAERMA) integrating deep learning and association rule mining.
result Achieved high classification accuracy (77% AUC, 77% SE, 68% SP) with 100 units.
In statistical genetics an important task involves building predictive models for the genotype-phenotype relationships and thus attribute a proportion of the total phenotypic variance to the variation in genotypes. Numerous models have been proposed to incorporate additive genetic effects into models for prediction or …