Article

ACMTF-R: supervised multi-omics data integration uncovering shared and distinct outcome-associated variation

2025-07-31

Abstract excerpt

The rapid growth of high-dimensional biological data has necessitated advanced data fusion techniques to integrate and interpret complex multi-omics and longitudinal datasets. Shared and unshared structure across such datasets can be identified in an unsupervised manner with Advanced Coupled Matrix and Tensor Factorization (ACMTF), but this cannot be related to an outcome. Conversely, N-way Partial Least Squares (...

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Literature Corpus work: 444b1843-47a5-5f85-b6d6-141daf8831e6
DOI: 10.1101/2025.07.28.667162

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