面向生物序列预测的课程学习：以从头肽测序为例
Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
AI总结：
针对现有非自回归Transformer肽测序模型依赖CTC损失易训练失败的问题，提出融入结构化蛋白质序列课程学习策略的改进模型，搭配自优化推理模块，大幅降低训练失败率，在9个基准物种的多指标上均优于现有方法。
摘要：
肽测序——即从质谱数据中识别氨基酸序列的过程——是蛋白质组学中的一项基础任务。非自回归Transformer（NAT）已被证明在该任务上效果显著，性能优于传统方法。与按顺序生成词元的自回归模型不同，NAT通过无掩码自注意力利用双向上下文，同时预测所有位置。然而，现有NAT方法通常依赖连接主义时间分类（CTC）损失，由于CTC的复杂性，其优化难度极大，还会增加训练失败的风险。\n为解决这些问题，我们提出了一种改进的非自回归肽测序模型，该模型融入了结构化的蛋白质序列课程学习策略。该方法通过采样过程，基于模型预估的蛋白质生成能力调整蛋白质的学习难度，让模型从简单序列到复杂序列逐步学习肽生成。此外，我们引入了一个自优化推理阶段模块，利用已学习的NAT词元嵌入迭代增强预测结果，在细粒度层面提升序列准确性。\n我们的课程学习策略在多种数据分布上的采样训练结果显示，将NAT训练失败的频率降低了90%以上。在9个基准物种上的评估表明，我们的方法在多个指标和物种上均优于所有现有方法。
英文摘要：
Peptide sequencing-the process of identifying amino acid sequences from mass spectrometry data-is a fundamental task in proteomics. Non-Autoregressive Transformers (NATs) have proven highly effective for this task, outperforming traditional methods. Unlike autoregressive models, which generate tokens sequentially, NATs predict all positions simultaneously, leveraging bidirectional context through unmasked self-attention. However, existing NAT approaches often rely on Connectionist Temporal Classification (CTC) loss, which presents significant optimization challenges due to CTC's complexity and increases the risk of training failures. To address these issues, we propose an improved non-autoregressive peptide sequencing model that incorporates a structured protein sequence curriculum learning strategy. This approach adjusts protein's learning difficulty based on the model's estimated protein generational capabilities through a sampling process, progressively learning peptide generation from simple to complex sequences. Additionally, we introduce a self-refining inference-time module that iteratively enhances predictions using learned NAT token embeddings, improving sequence accuracy at a fine-grained level. Our curriculum learning strategy reduces NAT training failures frequency by more than 90% based on sampled training over various data distributions. Evaluations on nine benchmark species demonstrate that our approach outperforms all previous methods across multiple metrics and species.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
