ArticleBMC bioinformatics2017
MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transcriptomics of interstitial lung disease: a systematic review and meta-analysis.The European respiratory journal · 2025Pooled it
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- Integrated workflow for univariate and multivariate evaluation of batch correction reliability.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.Statistics in medicine · 2026Article
- The middle ear-nasopharyngeal microbiome axis associated with obstructive Eustachian tube dysfunction in chronic otitis media.mSystems · 2026Article
- Exhaled breath volatiles for asthma diagnosis: discovery and validation in untreated but symptomatic patients.Scientific reports · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Unlocking the power of extracellular vesicles: multi-omics integration for cancer biomarker discovery.Biomarker research · 2026Review
- A cooperative learning framework for the integration of metabolomic data from multiple cohorts and common phenotype identification.Computational and structural biotechnology journal · 2026Article
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- Identification of a specific set of genes predicting obesity before phenotype appearance.iScience · 2025Article
- Microbial Interactions Influence the Chemical Defense of Wild and Cultivated Tomato Species.Journal of chemical ecology · 2025Article
- Multi-omic analysis of biological aging biomarkers in long-term calorie restriction and endurance exercise practitioners: A cross-sectional study.Aging cell · 2025Article
- Dual-approach co-expression analysis framework (D-CAF) enables identification of novel circadian co-regulation from multi-omic timeseries data.BMC bioinformatics · 2025Article
- Gene content of seawater microbes is a strong predictor of water chemistry across the Great Barrier Reef.Microbiome · 2025Article
- Single-cell Technology in Stem Cell Research.Current stem cell research & therapy · 2025Review
- On exploring cross-sectional stability and persistence of microbiome in a multiple body site colorectal cancer dataset.Frontiers in microbiology · 2025Article
- Multiomics of Aging and Aging-Related Diseases.International journal of molecular sciences · 2024Review
- Dual-Approach Co-expression Analysis Framework (D-CAF) Enables Identification of Novel Circadian Regulation From Multi-Omic Timeseries Data.bioRxiv : the preprint server for biology · 2024Article
2 more citing papers are in PubMed but not listed here.
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMolecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure; unwanted systematic variation removal techniques are applied prior to classification methods.
resultsTo limit the risk of overfitting and over-optimistic results of a two-step procedure, we developed a novel multivariate integration method, MINT, that simultaneously accounts for unwanted systematic variation and identifies predictive gene signatures with greater reproducibility and accuracy. In two biological examples on the classification of three human cell types and four subtypes of breast cancer, we combined high-dimensional microarray and RNA-seq data sets and MINT identified highly reproducible and relevant gene signatures predictive of a given phenotype. MINT led to superior classification and prediction accuracy compared to the existing sequential two-step procedures.
conclusionsMINT is a powerful approach and the first of its kind to solve the integrative classification framework in a single step by combining multiple independent studies. MINT is computationally fast as part of the mixOmics R CRAN package, available at http://www.mixOmics.org/mixMINT/ and http://cran.r-project.org/web/packages/mixOmics/ .
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