使用GANs进行谱系追踪的最佳传输
Optimal Transport using GANs for Lineage Tracing
摘要：
在本文中，我们提出了Super-OT，这是一种新的计算谱系追踪方法，它将监督学习框架与基于生成对抗网络（GANs）的最佳传输相结合。与以前的世系追踪方法不同，Super-OT具有集成配对数据的灵活性。我们基于单细胞RNA-seq数据对Super-OT进行了基准测试，Waddington-OT是一种流行的谱系追踪方法，也采用了最佳运输。我们表明，Super-OT在预测分化过程中细胞的类别结果方面比Waddington-OT获得了收益，因为它允许在训练过程中整合额外的信息。
英文摘要：
In this paper, we present Super-OT, a novel approach to computational lineage tracing that combines a supervised learning framework with optimal transport based on Generative Adversarial Networks (GANs). Unlike previous approaches to lineage tracing, Super-OT has the flexibility to integrate paired data. We benchmark Super-OT based on single-cell RNA-seq data against Waddington-OT, a popular approach for lineage tracing that also employs optimal transport. We show that Super-OT achieves gains over Waddington-OT in predicting the class outcome of cells during differentiation, since it allows the integration of additional information during training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
