Semi-Supervised Deep Matrix Factorization for Multi-Omics Clustering


 Semi-Supervised Deep Matrix Factorization for Multi-Omics Clustering


This study proposes a semi-supervised deep matrix factorization (SSDMF) model for integrative multi-omics clustering. By combining nonnegative matrix factorization, manifold regularization, and deep representation learning, the framework captures shared and modality-specific structures across genomic, transcriptomic, and proteomic datasets.

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