English(EN) Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening

新研究质疑AI预测基因相互作用的能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新研究论文探讨了机器学习模型从高通量扰动筛选中预测基因相互作用的有效性。该研究引入了“加性不一致性”（AD）作为一种度量标准，用于量化模型对未见基因组合预测的额外相互作用效应。在各种预测方法和数据集上，专门的神经网络预测器并未持续优于简单的加性基线，并且具有较高AD得分的预测往往误差更大。研究表明，AD可以作为指导代理扰动筛选工作流程中决策的可靠信号。 AI

影响挑战了当前机器学习模型在复杂生物学预测中的可靠性，并引入了一个新的模型性能评估指标。

排序理由该集群包含一篇在arXiv上发表的学术论文，详细介绍了研究结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Ziyuan Gao · 2026-10-15 04:00

扰动预测器能学习基因相互作用吗？诊断代理筛选中的外推法

arXiv:2610.18880v1 Announce Type: new Abstract: High-throughput perturbation screens increasingly use machine-learning models to predict the effects of gene combinations that were never measured. A difficult case arises when both genes in a test pair were seen individually during…

本文如何被排名

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扰动预测器能学习基因相互作用吗？诊断代理筛选中的外推法

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