Spatial transcriptomics captures molecular states within cells and their organisation in tissue. However, integrating fine-grained gene information with spatial context at scale remains challenging for existing foundation models. Here we present NexuST, a hierarchical foundation model that repeatedly interleaves gene-l...
Hai-Ping Liu, Qian Zhao, Li-Jing Lin et al.· bioRxiv· 0 citations
Electroencephalography (EEG) is recorded continuously over hours, with relevant dynamics spanning timescales from milliseconds to hours. Most EEG foundation models nevertheless process fixed windows independently, limiting their ability to capture information encoded in long-timescale dynamics. State-space architecture...
Yi-Fan Wang, Hai-Ping Liu, Yang Cui et al.· 0 citations
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, Li-Jing 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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