人工神经网络的免疫识别
Immunological recognition by artificial neural networks
AI总结：
本研究利用人工神经网络处理TCR与肽的序列信息，验证了ANNs能学习并泛化基于亲和力的免疫识别，并发现均匀编码氨基酸序列对监督学习更有效。
摘要：
T细胞受体（TCRs）与抗原肽之间的结合亲和力主要决定了免疫识别。T细胞仅依靠TCRs与抗原肽之间的综合结合亲和力来识别肽氨基酸的数字序列并非易事。为了解决这个问题，我们研究了处理受体和肽的数字实验氨基酸序列信息的人工神经网络（ANNs）是否能够学习并泛化基于亲和力的肽序列判别。一对TCR和肽序列对应于ANNs的输入，而免疫识别的成功或失败对应于输出。该输出由理论模型和实验数据共同获得。在两种情况下，我们都确认ANNs能够学习免疫识别。我们还发现，氨基酸序列的均匀编码对于监督学习任务更为有效。
英文摘要：
The binding affinity between the T-cell receptors (TCRs) and antigenic peptides mainly determines immunological recognition. It is not a trivial task that T cells identify the digital sequences of peptide amino acids by simply relying on the integrated binding affinity between TCRs and antigenic peptides. To address this problem, we examine whether the affinity-based discrimination of peptide sequences is learnable and generalizable by artificial neural networks (ANNs) that process the digital experimental amino acid sequence information of receptors and peptides. A pair of TCR and peptide sequences correspond to the input for ANNs, while the success or failure of the immunological recognition correspond to the output. The output is obtained by both theoretical model and experimental data. In either case, we confirmed that ANNs could learn the immunological recognition. We also found that a homogenized encoding of amino acid sequence was more effective for the supervised learning task.
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