  Predicting cross-immunity between viral strains is vital for public health
surveillance and vaccine development. Traditional neural network methods, such
as BiLSTM, could be ineffective due to the lack of lab data for model training
and the overshadowing of crucial features within sequence concatenation. The
current work proposes a less data-consuming model incorporating a pre-trained
gene sequence model and a mutual information inference operator. Our
methodology utilizes gene alignment and deduplication algorithms to preprocess
gene sequences, enhancing the model's capacity to discern and focus on
distinctions among input gene pairs. The model, i.e., DNA Pretrained
Cross-Immunity Protection Inference model (DPCIPI), outperforms
state-of-the-art (SOTA) models in predicting hemagglutination inhibition titer
from influenza viral gene sequences only. Improvement in binary cross-immunity
prediction is 1.58% in F1, 2.34% in precision, 1.57% in recall, and 1.57% in
Accuracy. For multilevel cross-immunity improvements, the improvement is 2.12%
in F1, 3.50% in precision, 2.19% in recall, and 2.19% in Accuracy. Our study
highlights the potential of pre-trained gene models in revolutionizing gene
sequence-related prediction tasks. With more gene sequence data being harnessed
and larger models trained, we foresee a significant impact of pre-trained
models on clinical and public health applications.
