ArticleNature methods2024
SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- IL4Rα blockade inhibits proliferation of malignant lymphocytes and the immunosuppressive tumor microenvironment of mycosis fungoides.Journal for immunotherapy of cancer · 2026Article
- Corneal Innervation Research at a Crossroads: A Tool-Driven Roadmap for the Future.Investigative ophthalmology & visual science · 2026Review
- Genetic and cross-species single-cell analyses prioritize CD83 in antigen-presenting cells as a candidate therapeutic target in asthma.Frontiers in immunology · 2026Article
- Sucralose Consumption Ablates Cancer Immunotherapy Response through Microbiome Disruption.Cancer discovery · 2025Article
- Article
- Interpretable machine learning coupled to spatial transcriptomics unveils mechanisms of macrophage-driven fibroblast activation in ischemic cardiomyopathy.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning approaches enable the discovery of therapeutics across domains.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.JCI insight · 2025Article
- Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies.Frontiers in immunology · 2025Review
- Agonism of the glutamate receptor GluK2 suppresses dermal mast cell activation and cutaneous inflammation.Science translational medicine · 2024Article
- STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics.Advances in neural information processing systems · 2024Article
- Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.JCI insight · 2024Article
- Spatial cytokine microniches direct T helper cell pathways that drive allergic asthma.Nature immunology · 2024Article
- Spatial microniches of IL-2 combine with IL-10 to drive lung migratory TNature immunology · 2024Article
- Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations.NPJ systems biology and applications · 2024Article
- Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.Science translational medicine · 2024Article
- Mining nucleic acid "omics" to boost liquid biopsy in cancer.Cell reports. Medicine · 2024Review
- Embedding AI in biology.Nature methods · 2024Article
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Authors and funding
14 authors.
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Abstract
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.
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