We developed Significant Latent Factor Interaction Discovery and Exploration (SLIDE), an interpretable machine learning approach that can infer hidden states (latent factors) underlying biological outcomes. These states capture the complex interplay between factors derived from multiscale, multiomic datasets across biological contexts and scales of resolution.
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References
Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023). This review article provides a good summary of modern multi-omic technologies.
Bing, X., Bunea, F., Royer, M. & Das, J. Latent model-based clustering for biological discovery. iScience 14, 125–135 (2019). Our previous work forms the basis for the unsupervised latent-factor discovery approach with identifiability guarantees used in this study.
Barber, R. F. & Candes, E. J. Controlling the false discovery rate via knockoffs. Ann. Stat. 43, 2055–2085 (2015). This paper lays out the theoretical framework that we use for false discovery rate control.
Bzdok, D., Engemann, D. & Thirion, B. Inference and prediction diverge in biomedicine. Patterns (N Y) 1, 100119 (2020). This study presents a perspective on the trade-offs between inference and prediction.
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This is a summary of: Rahimikollu, J. et al. SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains. Nat. Methods https://doi.org/10.1038/s41592-024-02175-z (2024).
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Uncovering hidden states driving biological outcomes using machine learning. Nat Methods 21, 758–759 (2024). https://doi.org/10.1038/s41592-024-02176-y
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DOI: https://doi.org/10.1038/s41592-024-02176-y