Abstract: A method may include generating multiple virtual progenies from multiple first virtual gametes and multiple second virtual gametes. Each virtual progeny may combine one of the first virtual gametes and one of the second virtual gametes. A computing server may input, for each virtual progeny, data associated with the first virtual gamete of the virtual progeny to a machine learning model to determine a first variant-specific gene dysfunction score corresponding to a target allele site. The computing server may also input, for each virtual progeny, data associated with the second virtual gamete of the virtual progeny to the machine learning model to determine a second variant-specific gene dysfunction score corresponding to the target allele site. The computing server may derive, for each virtual progeny, a dysfunction likelihood score of the target allele site from the first variant-specific gene dysfunction score and the second variant-specific gene dysfunction score.

Abstract: A device, system and method for predicting gene-dysfunction caused by a genetic mutation in the genome of an organism. A neural network may comprise multiple nodes respectively associated with multiple different gene-dysfunction metrics and multiple different confidence weights. The neural network may combine the multiple gene-dysfunction metrics according to the respective associated confidence weights to generate one or more likelihoods that a genetic mutation causes gene-dysfunction in organisms. In a training-phase, the neural network may be trained using an input data set including genetic mutations to generate new gene-dysfunction metrics and new associated confidence weights that optimize the neural network based on a cost factor.