研究人员开发了一种新颖的多模态框架来创建亚细胞分辨率单细胞嵌入。该方法整合了RNA表达谱、蛋白质序列数据和蛋白质结构信息。通过利用交叉注意力架构，该框架模拟了不同亚细胞区室内的相互作用，与以往将细胞整体处理的方法相比，提供了更精细的细胞表示。 AI
排序理由 该集群包含一篇arXiv预印本，详细介绍了用于生物数据分析的新研究框架。
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新框架创建亚细胞分辨率单细胞嵌入
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- 该集群包含一篇arXiv预印本，详细介绍了用于生物数据分析的新研究框架。
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报道来源 [2]
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利用转录组数据、蛋白质结构和定位信息进行亚细胞分辨率单细胞嵌入学习
arXiv:2609.02344v1 Announce Type: cross Abstract: Existing cell embedding methods predominantly rely on transcriptomic or proteomic measurements and represent each cell as a holistic entity, thereby overlooking the subcellular localization of individual molecules. Moreover, they …
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利用转录组数据、蛋白质结构和定位信息进行亚细胞分辨率单细胞嵌入学习
Existing cell embedding methods predominantly rely on transcriptomic or proteomic measurements and represent each cell as a holistic entity, thereby overlooking the subcellular localization of individual molecules. Moreover, they rarely incorporate protein structural information,…