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Review Article

使用 Transformer 获得更好的性能：CPPFormer 在细胞穿透肽的精确预测中

卷 29, 期 5, 2022

发表于: 14 January, 2022

页: [881 - 893] 页: 13

弟呕挨: 10.2174/0929867328666210920103140

价格: $65

摘要

由于其卓越的性能，基于“编码器-解码器”范式的Transformer模型已成为自然语言处理的主流模型。然而，生物信息学已经接受了机器学习，并在药物设计和蛋白质特性预测方面取得了显着进展。细胞穿透肽 (CPP) 是一种可渗透的蛋白质，是药物穿透任务中方便的“邮递员”。然而，仅发现了少数CPP，限制了它们在药物渗透性方面的实际应用。 CPP 导致了一种新方法，该方法能够仅将大分子吸收到细胞中（即，在药物中没有发现其他潜在有害物质）。以前的大多数研究都利用简单的机器学习技术和手工制作的特征来构建一个简单的分类器。 CPPFormer 是通过实现 Transformer 的注意力结构，根据 CPP 的短长度的特点重建网络，并使用带有一些人工设计特征的自动特征提取器来共同指导预测结果而构建的。与以往所有方法和其他经典文本分类模型相比，实证结果表明，我们提出的基于深度模型的方法取得了最佳性能，在 CPP924 数据集上的准确率为 92.16%，并且通过了各种指标测试。

关键词: 细胞穿透肽、深度学习、药物穿透、变压器、特征提取器、分类。

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