Abstract: Methods and systems for expanding a training set of one or more original biological sequences are provided. An original training set is obtained, wherein the original training set comprises one or more original biological sequences. Saliency values corresponding to one or more elements in each of the one or more original biological sequences are obtained. For each of the original biological sequences, one or more modified biological sequences are produced and the one or more modified biological sequences are associated with the original biological sequence. One or more elements are generated 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. The one or more modified biological sequences for each of the original biological sequences are added to the original training set to form an expanded training set.

Abstract: Methods and systems for expanding a training set of one or more original biological sequences are provided. An original training set is obtained, wherein the original training set comprises one or more original biological sequences. Saliency values corresponding to one or more elements in each of the one or more original biological sequences are obtained. For each of the original biological sequences, one or more modified biological sequences are produced and the one or more modified biological sequences are associated with the original biological sequence. One or more elements are generated 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. The one or more modified biological sequences for each of the original biological sequences are added to the original training set to form an expanded training set.

Abstract: The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.