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Found 2 papers, 1 papers with code
Improving genetic risk prediction across diverse population by disentangling ancestry representations
This can lead to biases in the risk predictors resulting in poor generalization when applied to minority populations and admixed individuals such as African Americans.
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants
The merit of the proposed method includes: (1) flexible modelling of the non-linear effect of genetic variants to improve statistical power; (2) multiple knockoffs in the input layer to rigorously control false discovery rate; (3) hierarchical layers to substantially reduce the number of weight parameters and activations to improve computational efficiency; (4) de-randomized feature selection to stabilize identified signals.
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