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  • scNODE: Generative Model for Temporal Single Cell . . . - GitHub
    Raw and preprocessed data of three scRNA-seq datasets can be downloaded from here All model predictions on three datasets are available at here Experiment results for downstream analysis are available at here
  • scNODE: generative model for temporal single cell transcriptomic data . . .
    We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space
  • scNODE : Generative Model for Temporal Single Cell . . . - Europe PMC
    Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods
  • 学生信息系统 - SDPT
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  • 单细胞预训练模型工作总结 (1) - RuiRui的博客 | RuiRui Blog
    最近想借助transformer架构去解决手头数据的建模分析问题,同时,本着“击败敌人首先要了解敌人”的思想,想要证实我的一些大逆不道想法,还是要深入了解一下scRNA-seq预训练模型这一两年来的进展。 这篇博客是对近期的一些论文阅读的总结。 首先感谢OmicsML ( https: github com OmicsML awesome-foundation-model-single-cell-papers )这个开源项目,将这几年来的一些预训练模型 (foundation model)做了一个详尽的收录,我也是参考里面的论文列表去阅读的。 下面将尽可能按照时间顺序去总结一下这些工作。
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  • scNODE : generative model for temporal single cell transcriptomic data . . .
    This work proposes scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints and integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and non-linear latent space


















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