English(EN) Robust Machine Learning for Regulatory Sequence Modeling under Biological and Technical Distribution Shifts

新框架测试基因组序列预测中AI模型的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一个新框架，用于评估基因组学中机器学习模型的鲁棒性，特别是用于监管序列建模。研究强调，虽然像CNN和Transformer这样的深度学习模型在标准条件下表现良好，但在面对真实世界分布变化（如细胞类型、测定协议或测序伪影的变化）时，它们会表现出显著的性能下降和校准失败。提出的框架结合了机械模拟基准和真实数据分析，以识别这些鲁棒性差距，并提出结合生物结构先验和不确定性感知的选择性预测可以提高模型可靠性。 AI

影响强调了当前基因组学ML模型中关键的鲁棒性差距，可能指导未来开发更可靠的生物序列分析。

排序理由该集群包含一篇详细介绍评估机器学习模型新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Yiyao Yang · 2026-10-12 04:00

面向生物和技术分布偏移的稳健机器学习在调控序列建模中的应用

arXiv:2601.14969v3 Announce Type: replace-cross Abstract: Robust machine learning for regulatory genomics is studied under biologically and technically induced distribution shifts. Deep convolutional and attention based models achieve strong in distribution performance on DNA reg…

本文如何被排名

报道来源 [1]

面向生物和技术分布偏移的稳健机器学习在调控序列建模中的应用

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