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Since MiHAs can be used as therapeutic T cell targets to augment the graft-versus-tumor effects (GvT), it attracts intensive research both in precision medicine and vaccine areas. Attributed to the accumulate of MHC-peptides binding data from traditional methods and the advanced deep learning techniques, by leverage the convenience of modeling, the in-silico design approaches accelerate the MiHA research dramatically.

Several prediction models were developed by our marvelous staffs, we were not only staying at the bioinformatics level but involved Machine learning including different comparative models between Linear regression, Naive Bayes, and the SVM. Also, we use CNN for accurate image recognition and NLP for sequence generation. The results of these predictions provide valuable information for MiHA research to make important decisions for further strategic plans with fewer blind spots. Welcome to more discover.

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