1. An information processing system for reducing search time of features in sample data and reducing computation time when training a model for generating specialized data based on the features, the information processing system comprising: a memory; a processor communicatively coupled to the memory; and a feature selection circuit coupled to the memory and the processor, wherein the feature selection circuit is configured to perform a plurality of operations comprising: receiving a set of genetic markers and a phenotype from at least one external information processing system; training an epistasis effect model based on the set of genetic markers and the phenotype; reducing computation time of the information processing system during the training of the epistasis effect model and further reducing feature selection time of the epistasis effect model, wherein reducing the computation time and the feature selection time comprises: determining, for each of the set of genetic markers, a relevance score with respect to the phenotype according to I(x j training ;c training ), where I is mutual information between a given genetic marker x j and a phenotype c, where mutual information I between two variables x and y is defined, based on their joint marginal probabilities p(x) and p(y) and probabilistic distribution p(x, y), as: I ⁡ ( x , y ) = ∑ i , j ⁢ p ⁡ ( x i , y i ) ⁢ log ⁢ p ⁢ ( x i , y i ) p ⁡ ( x i ) ⁢ p ⁡ ( y i ) ; setting a threshold based on the relevance score of a genetic marker in the set of genetic markers with a highest relevancy score; determining, for at least one individual genetic marker in the set of genetic markers having a relevance score satisfying the threshold, a relevance score for at least one interaction between the at least one individual genetic marker and at least one other individual genetic marker in the set of genetic markers; adding the at least one interaction to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold, wherein the top-k feature set comprises one or more markers and one or more interactions, and wherein each of the one or more genetic markers and each of the one or more interactions comprises a top-k relevance score; identifying a subset of the top-k feature set based on the set of genetic markers, wherein each feature in the subset of the top-k feature set maximizes a relevancy with the phenotype and minimizes a redundancy with respect to other selected features; and training the epistasis effect model utilizing at least the subset of the top-k feature set, wherein the epistasis effect model predicts phenotypes for genetic markers; storing the trained epistasis effect model in memory; electronically obtaining, a new set of a set of genetic markers that is associated phenotype data; and executing the trained epistasis effect model, wherein executing the trained epistasis effect model comprises inputting the new set of genetic markers into the trained epistasis effect model; and outputting a phenotype for the new set of genetic markers, wherein the phenotype that was outputted was not made available to the feature selection circuit as part of the new set of genetic markers.