Tumor neoantigen prediction method and prediction device and storage medium

Abstract

The invention relates to a tumor neoantigen prediction method and prediction device based on chromatin high-level conformation and deep sparse learning, and a storage medium, and the method comprisesthe steps: building a deep neural network prediction model based on group selection, and carrying out the training of the model through training data to obtain an immunogenicity predicted value of a to-be-predicted object (namely, a potential tumor neoantigen peptide), wherein each sample adopted when the deep neural network prediction model is trained comprises chromatin 3D conformation information and features generated based on a polypeptide amino acid sequence. Compared with the prior art, the method has the advantages of high prediction precision, convenience in prediction and the like.

Description

technical field [0001] The present invention relates to the field of neoantigen prediction in personalized tumor immunotherapy, in particular to a tumor neoantigen prediction method, prediction device and storage medium based on high-level chromatin conformation and deep sparse learning. Background technique [0002] At present, the conventional treatment of cancer patients mainly relies on non-individualized surgical resection, radiotherapy and chemotherapy, and targeted drug therapy. Transiently prolongs the survival of cancer patients. [0003] In recent years, the method of tumor immunotherapy by targeting the patient's tumor cells through the tumor patient's own immune system has entered people's field of vision. In individualized tumor immunotherapy, tumor patient-specific target molecules that play a key role are called tumor neoantigens. The essence of tumor neoantigen is protein, which is produced by mutation of tumor genome. Because it contains non-synonymous mut...

AI Technical Summary

Problems solved by technology

However, the current tumor neoantigen selection technology still has many technical problems, such as heavy selection workload and low precision.

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