基于Transformer神经网络的丙型肝炎病毒基因分型
Hepatitis C Virus Genotyping with a Transformer Neural Network
AI总结：
本研究微调Transformer神经网络，利用2881条HCV序列实现基因型及亚型分类，明确了数据片段化、分布等对性能的影响，为HCV基因分型提供了有前景的方法。
AI中文摘要：
本研究旨在探究基于Transformer的模型在基因序列分类中的适用性，通过评估其微调后预测丙型肝炎病毒（HCV）基因型及亚型的性能来实现。研究使用了从洛斯阿拉莫斯HCV序列数据库获取的共2881条HCV全基因组序列，包含1至6基因型及所有确认亚型，因样本量不足排除了7、8基因型。微调过程基于多个数据集，这些数据集在片段化方法、单文件数据量及标签方面存在差异。在基因型分类中，采用均匀片段化和均衡样本分布的微调策略性能更高，精度范围为98.48%至100%；相比之下，采用导致数据不平衡的片段化策略且样本在训练文件中任意分布的微调，精度仅为48.12%，与其他模型相比较低，该配置经人工评估后，因基因型5在所用数据集中频率低，导致基因型5预测错误率高。在亚型分类中，性能最佳的微调方法达到99.89%的准确率和99.87%的精度；包含更多基因型的模型因任务复杂度增加，性能略有下降。本研究表明，当微调数据集包含适当片段化、分布及标签的基因序列时，基于Transformer的神经网络可实现高性能，是HCV基因型及亚型分类的有前景方法。
英文摘要：
This study aims to explore the applicability of Transformer-based models for genetic sequence classification by evaluating their performance in predicting hepatitis C virus (HCV) genotypes and subtypes after fine-tuning. A total of 2,881 HCV whole-genome sequences obtained from the Los Alamos HCV Sequence Database were used, including genotypes 1 to 6 and all confirmed subtypes. Genotypes 7 and 8 were excluded due to an insufficient number of samples. The fine-tuning process was based on several datasets that differed in fragmentation method, data volume per file, and labeling. In genotype classification, fine-tuning strategies employing homogeneous fragmentation and balanced sample distribution resulted in higher performance, with precision ranging from 98.48% to 100%. In contrast, fine-tuning conducted using a fragmentation strategy that caused data imbalance, along with an arbitrary distribution of samples across training files, achieved a precision of 48.12%, which is considered low compared with other models. This configuration, which was also manually evaluated, resulted in a high error rate in genotype 5 prediction due to its low frequency in the datasets used. In subtype classification, the best-performing fine-tuning approach achieved 99.89% accuracy and 99.87% precision. Models that included additional genotypes showed a slight decrease in performance due to the increased complexity of the task. This study demonstrates that, when fine-tuning datasets contain properly fragmented, distributed, and labeled genetic sequences, Transformer-based neural networks can achieve high performance and are a promising approach for HCV genotype and subtype classification.
