ArticleBioinformatics (Oxford, England)2020
A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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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Who cites it
22 citing papers in PubMed.
- Exposure-aware multi-omics and artificial intelligence for biomarker discovery and precision prevention in diffuse glioma.Frontiers in immunology · 2026Review
- Per- and Polyfluoroalkyl Substances, Serum Lipidome, and Clinical Outcomes after Percutaneous Coronary Intervention in Type 2 Diabetic Patients: A Prospective Nested Case-control Study.Journal of atherosclerosis and thrombosis · 2025Article
- Translational framework linking perfluoroheptanoic acid (PFHpA) exposure to metabolic dysfunction associated steatotic liver disease in adolescents.Communications medicine · 2025Article
- Placental DNA methylation key topics: sex- and cell-type specificity, mediation, multi-omics, and biomarker discovery.Epigenomics · 2025Review
- Dysregulated maternal and newborn fatty acid, sugar and amino acid metabolism associated with high birth weight.International journal of obesity (2005) · 2025Article
- Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.Environment international · 2024Article
- Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions.Statistics in biosciences · 2024Article
- Epigenetic Responses to Nonchemical Stressors: Potential Molecular Links to Perinatal Health Outcomes.Current environmental health reports · 2024Review
- An extension of latent unknown clustering integrating multi-omics data (LUCID) incorporating incomplete omics data.Bioinformatics advances · 2024Article
- Prenatal exposures to endocrine disrupting chemicals: The role of multi-omics in understanding toxicity.Molecular and cellular endocrinology · 2023Review
- An epidemiological introduction to human metabolomic investigations.Trends in endocrinology and metabolism: TEM · 2023Review
- Molecular mechanisms of environmental exposures and human disease.Nature reviews. Genetics · 2023Review
- Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics.Metabolites · 2023Article
- FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.Bioinformatics (Oxford, England) · 2022Article
- Applications of Omics Technology for Livestock Selection and Improvement.Frontiers in genetics · 2022Review
- In Utero Exposure to Mercury Is Associated With Increased Susceptibility to Liver Injury and Inflammation in Childhood.Hepatology (Baltimore, Md.) · 2021Article
- The geometry of clinical labs and wellness states from deeply phenotyped humans.Nature communications · 2021Article
- Prenatal Exposure to Perfluoroalkyl Substances Associated With Increased Susceptibility to Liver Injury in Children.Hepatology (Baltimore, Md.) · 2020Article
- Omics Application in Animal Science-A Special Emphasis on Stress Response and Damaging Behaviour in Pigs.Genes · 2020Review
- Association of Fish Consumption and Mercury Exposure During Pregnancy With Metabolic Health and Inflammatory Biomarkers in Children.JAMA network open · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
motivationEpidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups.
resultsWe propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrated model for future prediction of risk subgroups and phenotypes. We apply this approach to two real data applications to highlight the integration of genomic, exposure and metabolomic data. AVAILABILITY AND IMPLEMENTATION: The LUCID method is implemented through the LUCIDus R package available on CRAN (https://CRAN.R-project.org/package=LUCIDus). SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.
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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.