The present invention discloses a method for predicting adaptive immune receptors by integrating
gene and sequence information, comprising the following steps: S1, modifying the SC-AIR-BERT model to construct an SC-AIR-BERT-Multi model; the SC-AIR-BERT-Multi model comprises a
gene information extraction channel, a sequence
information extraction channel, a multimodal fusion module, and two multi-layer perceptrons for multi-
task learning; S2, in the
gene information extraction channel, using the gene name as input, obtaining the
gene feature h of the immune
cell receptor gene ; S3, in the sequence information extraction channel, using TCR sequence or BCR sequence as input, obtain the
sequence feature h of immune
cell receptor seq ; S4, the gene features and sequence features of the V, D, and J gene segments are sent to the multimodal
feature fusion module for fusion, and the fused features are generated; S5, the multimodal
receptor feature Representation learned in step S4 is mapped to the final TCR or BCR
antigen binding specificity prediction and affinity prediction results through two multi-layer perceptrons for prediction.