English(EN) The Accuracy Paradox: Empirical Diagnostic of Default Decision Thresholds in Multi-Label Enzyme Commission Prediction [With Code]

研究论文揭示酶预测模型中的准确性悖论

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

一篇题为“准确性悖论”的新研究论文强调了用于预测酶委员会（EC）编号的标准机器学习管道存在严重问题。研究发现，尽管一个系统取得了77.16%的总体高准确率，但其在特定EC类别（尤其是EC6）上的表现严重受损，召回率为0.00%。研究人员强调，默认决策阈值掩盖了生物信息学工作流程中的重大错误，并主张针对特定目标的阈值优化和事后一致性校准是可靠机器学习应用的关键保障措施。 AI

影响强调了标准机器学习在科学应用中的关键缺陷，需要改进校准以实现可靠的生物信息学。

排序理由学术论文，详细介绍了机器学习模型性能的诊断研究。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Bilal Ahmad, Rajed Mehmood · 2026-09-10 04:00

准确性悖论：多标签酶委员会预测中默认决策阈值的经验诊断 [含代码]

arXiv:2609.07897v1 Announce Type: cross Abstract: Automated prediction of Enzyme Commission (EC) numbers plays a central role in functional annotation and computational drug discovery. However, standard multi-label machine learning pipelines frequently rely on default decision th…

本文如何被排名

报道来源 [1]

准确性悖论：多标签酶委员会预测中默认决策阈值的经验诊断 [含代码]

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