English(EN) Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy

跨物种神经学习使小鼠和人类大脑动力学对齐

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

研究人员开发了一种新颖的双规则对比学习框架，用于跨物种对齐神经活动表征，旨在提高对神经系统疾病药物疗效的预测能力。该方法通过基于生物状态而非物种来组织表征，从而学习共享的神经动力学，成功识别出小鼠和人类之间保守的结构。该框架通过将接受治疗的动物投射到跨物种表征中，证明了其解决小鼠癫痫模型与人类患者群体之间不同关系的能力，并回顾性地追踪了十种模型-药物组合已知的临床药物疗效。 AI

影响这项研究通过提高从动物模型到人类神经系统疾病研究结果的转化能力，有望带来更准确的临床前药物测试。

排序理由该集群包含一篇在arXiv上发表的学术论文，详细介绍了一种新的研究方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Marko Tvrdic, Justin Richmond Domingo, Jae Ann Buenaluz, Jydell Ashley Palomo Penollar, Edmayelle Villavicencio Alforja, Jobi Fallaeria Subosa, Gabriel Ocana-Santero · 2026-10-09 04:00

跨物种表征学习使小鼠和人类神经动力学保持一致并追踪临床药物疗效

arXiv:2610.11222v1 Announce Type: cross Abstract: Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of translational information because it captures high-dimensional variation in nervous-sy…

本文如何被排名

报道来源 [1]

跨物种表征学习使小鼠和人类神经动力学保持一致并追踪临床药物疗效

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