The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens’ immunogenicity.

中文翻译：

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DeepAntigen：一种通过3D基因组和深度稀疏学习进行新抗原优先排序的新方法。

癌症的突变可以以T细胞可识别的免疫原性肽（也称为新抗原）的形式编码其自身破坏的种子。然而，要根据潜在的新抗原候选物激活T细胞免疫反应的能力准确地排列其优先次序在计算上具有挑战性，特别是在体细胞突变丰富的情况下。尽管已提出了一些新抗原优先排序方法来解决此问题，但仍缺少专门设计用于解决此问题的高级机器学习模型。此外，现有的方法均未从3D基因组的角度考虑新抗原的原始DNA位点，而这可能为推断新抗原的免疫原性提供关键信息。