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English(EN) Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning

新的基础模型Tabula采用联邦学习实现隐私保护的单细胞基因组学

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Tabula，这是一种新颖的单细胞基因组学基础模型，它通过利用联邦学习来解决隐私问题。与以前的方法不同，该模型明确处理单细胞数据固有的表格结构。Tabula通过Chiron平台部署，能够在不共享原始数据的情况下实现跨机构的协作训练。该模型在各种生物学基准测试中表现出色，揭示了造血和神经发生等系统中的调控逻辑，并识别了人类成纤维细胞的潜在复壮因素。 AI

影响 增强了生物数据分析的隐私性，并推动了表格生物学数据的基础模型发展。

排序理由 该集群描述了一篇关于特定科学领域新基础模型的科学论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的基础模型Tabula采用联邦学习实现隐私保护的单细胞基因组学

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该集群描述了一篇关于特定科学领域新基础模型的科学论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiayuan Ding, Jianhui Lin, Ziyang Miao, Nils Mechtel, Shiyu Jiang, Yixin Wang, Zhaoyu Fang, Jorge D. Martin-Rufino, Chen Weng, Reuben Saunders, Weize Xu, Jonathan S. Weissman, Min Li, Jiliang Tang, Wei Ouyang, Yuancheng Ryan Lu, Xiaojie Qiu ·

    用于基因调控和衰老的预测性单细胞基础模型，结合隐私保护的表格学习

    arXiv:2607.19400v1 Announce Type: new Abstract: Pre-trained foundation models (FMs) have begun transforming single-cell genomics, but scaling them raises privacy concerns. Moreover, unlike text data, single-cell data is unordered and exhibits a unique tabular structure that curre…

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