A computer-implemented method of identifying pathogenic single nucleotide polymorphisms, SNPs, including: processing unlabeled SNPs in cycles using an ensemble of convolutional neural networks, CNNs, wherein the cycles include a first phase and a second phase; wherein the unlabeled SNPs include combinatorically generated SNP variants that are not labeled benign and have not been added to elite predicted pathogenic SNPs; processing in the first phase of a cycle includes: selecting separate pseudo-pathogenic training examples per CNN in the ensemble from the unlabeled SNPs, to use in combination with elite predicted pathogenic SNPs, if any; and training the ensemble of CNNs using common benign training examples of SNPs, balanced with the selected pseudo-pathogenic training examples and the elite predicted pathogenic SNPs, if any, thereby updating parameters of the CNNs in the ensemble; processing in the second phase of the cycle includes: applying the ensemble of CNNs with the updated parameters to generate pathogenicity scores for unlabeled SNPs not involved in the first phase; using the pathogenicity scores to identify outlier SNPs; and accumulating the outlier SNPs in the elite predicted pathogenic SNPs; performing the first phase and second phase processing in a first cycle includes: beginning with no elite predicted pathogenic SNPs; and ending with the identified outlier SNPs accumulated as the elite predicted pathogenic SNPs in the second phase processing; repeating the first phase and second phase processing in second and subsequent cycles includes: beginning with the elite predicted pathogenic SNPs identified as outliers in the second phase processing of prior cycles; and ending with additional identified outlier SNPs accumulated with the elite predicted pathogenic SNPs; and repeating the cycles until a final cycle after which a number of the elite predicted pathogenic SNPs accumulated in the second phase processing is within a predetermined spread of a number of SNPs in the common benign training examples. processing unlabeled SNPs in cycles using an ensemble of convolutional neural networks, CNNs, wherein the cycles include a first phase and a second phase; wherein the unlabeled SNPs include combinatorically generated SNP variants that are not labeled benign and have not been added to elite predicted pathogenic SNPs; processing in the first phase of a cycle includes: selecting separate pseudo-pathogenic training examples per CNN in the ensemble from the unlabeled SNPs, to use in combination with elite predicted pathogenic SNPs, if any; and training the ensemble of CNNs using common benign training examples of SNPs, balanced with the selected pseudo-pathogenic training examples and the elite predicted pathogenic SNPs, if any, thereby updating parameters of the CNNs in the ensemble; selecting separate pseudo-pathogenic training examples per CNN in the ensemble from the unlabeled SNPs, to use in combination with elite predicted pathogenic SNPs, if any; and training the ensemble of CNNs using common benign training examples of SNPs, balanced with the selected pseudo-pathogenic training examples and the elite predicted pathogenic SNPs, if any, thereby updating parameters of the CNNs in the ensemble; processing in the second phase of the cycle includes: applying the ensemble of CNNs with the updated parameters to generate pathogenicity scores for unlabeled SNPs not involved in the first phase; using the pathogenicity scores to identify outlier SNPs; and accumulating the outlier SNPs in the elite predicted pathogenic SNPs; applying the ensemble of CNNs with the updated parameters to generate pathogenicity scores for unlabeled SNPs not involved in the first phase; using the pathogenicity scores to identify outlier SNPs; and accumulating the outlier SNPs in the elite predicted pathogenic SNPs; performing the first phase and second phase processing in a first cycle includes: beginning with no elite predicted pathogenic SNPs; and ending with the identified outlier SNPs accumulated as the elite predicted pathogenic SNPs in the second phase processing; beginning with no elite predicted pathogenic SNPs; and ending with the identified outlier SNPs accumulated as the elite predicted pathogenic SNPs in the second phase processing; repeating the first phase and second phase processing in second and subsequent cycles includes: beginning with the elite predicted pathogenic SNPs identified as outliers in the second phase processing of prior cycles; and ending with additional identified outlier SNPs accumulated with the elite predicted pathogenic SNPs; and beginning with the elite predicted pathogenic SNPs identified as outliers in the second phase processing of prior cycles; and ending with additional identified outlier SNPs accumulated with the elite predicted pathogenic SNPs; and repeating the cycles until a final cycle after which a number of the elite predicted pathogenic SNPs accumulated in the second phase processing is within a predetermined spread of a number of SNPs in the common benign training examples.