BMCST: Balanced multi-view clustering for spatially resolved transcriptomics with Mamba-driven dynamic feature refinement

Yanran Zhu

doi:10.1016/j.inffus.2025.103425

BMCST: Balanced multi-view clustering for spatially resolved transcriptomics with Mamba-driven dynamic feature refinement

2025-06-27

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DOI：10.1016/j.inffus.2025.103425

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• A novel deep multi-view clustering network for SRT data termed as BMCST is proposed in this paper. • A Mamba-driven Dynamic Feature Refinement (MDFR) module is employed to optimize feature selection. • The unsupervised dominant view mining mechanism is utilized to tackle the view imbalance inherent in SRT data. • Extensive experiments demonstrate the outstanding performance of the proposed network.

BMCST: Balanced multi-view clustering for spatially resolved transcriptomics with Mamba-driven dynamic feature refinement

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