ArticlePatterns (New York, N.Y.)2022
Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Improving recombinant protein productivity in CHO cells via multi-omics data integration.Bioresources and bioprocessing · 2026Review
- IL4Rα blockade inhibits proliferation of malignant lymphocytes and the immunosuppressive tumor microenvironment of mycosis fungoides.Journal for immunotherapy of cancer · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Artificial intelligence and machine learning in immunosenescence: from biomarker discovery to clinical translation.Frontiers in aging · 2026Review
- Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.Science translational medicine · 2024Article
- Data-Driven Approaches to Predict Dendrimer Cytotoxicity.ACS omega · 2024Article
- SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.Nature methods · 2024Article
- From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches.Human vaccines & immunotherapeutics · 2023Review
- Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.Cell reports. Medicine · 2022Review
- Causal Discovery in High-dimensional, Multicollinear Datasets.Frontiers in epidemiology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.