A computer-implemented method for training a supervised machine learning model with an expanded training set using biological sequences, comprising: obtaining an original training set, wherein the original training set comprises one or more original biological sequences; obtaining saliency values corresponding to one or more elements in each of the one or more original biological sequences, wherein the saliency value corresponding to an element of the one or more elements indicates a degree of pertinence of the element to biological function, and wherein the saliency values are derived from one or more of: evolutionary conservation across at least two different species, allele frequency in a human population of at least two humans, DNA accessibility, ChIP-Seq, CLIP-Seq, SELEX, massively parallel reporter assays and mutational studies; for each of the one or more original biological sequences, producing one or more modified biological sequences and associating the one or more modified biological sequences with the original biological sequence, wherein each of the one or more original biological sequences has an associated label, and wherein each of the one or more modified biological sequences is associated with the same label as the associated original biological sequence; generating one or more elements in each of the one or more modified biological sequences using one or more elements in the associated original biological sequence and the corresponding saliency values; adding the one or more modified biological sequences for each of the one or more original biological sequences to the original training set to form an expanded training set; and training the supervised machine learning model using the expanded training set. obtaining an original training set, wherein the original training set comprises one or more original biological sequences; obtaining saliency values corresponding to one or more elements in each of the one or more original biological sequences, wherein the saliency value corresponding to an element of the one or more elements indicates a degree of pertinence of the element to biological function, and wherein the saliency values are derived from one or more of: evolutionary conservation across at least two different species, allele frequency in a human population of at least two humans, DNA accessibility, ChIP-Seq, CLIP-Seq, SELEX, massively parallel reporter assays and mutational studies; for each of the one or more original biological sequences, producing one or more modified biological sequences and associating the one or more modified biological sequences with the original biological sequence, wherein each of the one or more original biological sequences has an associated label, and wherein each of the one or more modified biological sequences is associated with the same label as the associated original biological sequence; generating one or more elements in each of the one or more modified biological sequences using one or more elements in the associated original biological sequence and the corresponding saliency values; adding the one or more modified biological sequences for each of the one or more original biological sequences to the original training set to form an expanded training set; and training the supervised machine learning model using the expanded training set.