This paper shows that latent-space predictive pretraining can provide a scalable route to foundation models for spatial transcriptomics. Existing spatial transcriptomics foundation models primarily reconstruct masked gene identities or expression values, potentially encouraging the reproduction of assay-specific techni...
Hai-Ping Liu, Qian Zhao, Lijing Lin et al.· 0 citations
This review systematically analyzes the emerging landscape of FMs in omics research, spanning sequence modeling, cell state characterization, and multimodal integration, and proposes a roadmap for the next generation of FMs, advocating for architectures that move beyond statistical correlation to incorporate causal rea...
Haozhe Liu, Wenhao Cai, Yizheng Sun et al.· Briefings in Bioinformatics· 0 citations
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