FIELD: biotechnology.
SUBSTANCE: methods for training a generative-adversarial network (hereinafter – GAN) in combination with a convolutional neural network (hereinafter – CNN) are disclosed. GAN and CNN can be trained using biological data, such as protein interaction data. CNN can be used to identify new data as positive or negative. Methods for the synthesis of polypeptide associated with new data on interaction of proteins identified as positive are disclosed.
EFFECT: GAN-CNN prediction of MHC-peptide binding.
25 cl, 17 dwg, 2 tbl, 6 ex
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