ReviewMolecular omics2021
Multi-omics data integration considerations and study design for biological systems and disease.
Review in Molecular omics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
90 citing papers in PubMed, 1 synthesis or guideline pooled it, 208 citations in OpenAlex.
- Graph databases in systems biology: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Reconstructing bovine disease trajectories through integrative multi-omics: molecular decision nodes, predictive biomarkers and precision intervention.Veterinary research communications · 2026Review
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- Integrated Metabolomic and Proteomic Analyses Reveal Differential Regulation of the Citrate Cycle and Pentose Phosphate Pathway byMetabolites · 2026Article
- Integrating omics and artificial intelligence in pediatric environmental health: tools, challenges, and cohort-based insights.Pediatric research · 2026Review
- Decoding Microbiota-Immune Interplay in Viral Pathogenesis: Toward Next-Generation Antiviral Therapies.Probiotics and antimicrobial proteins · 2026Review
- Advances in biotechnological methods for genetic and metabolic engineering in Methylomonas sp. DH-1.Journal of biological engineering · 2026Review
- Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.International urology and nephrology · 2026Article
- MCVAE-based multi-omic anomaly detection in Fragile X Syndrome.NAR molecular medicine · 2026Article
- Review
- Radiation-induced network rewiring in cancer: systems-level insights into environmental radioactivity and precision oncology.Frontiers in bioinformatics · 2026Review
- Advancing precision immuno-oncology in melanoma: the synergistic convergence of personalized neoantigen vaccines and multi-omics biomarker profiling.Frontiers in immunology · 2026Review
- Multiomics Approaches for Bacteriophage-Based Biocontrol Applications: A Review of Metabolomics, Transcriptomics, and Proteomics.BioMed research international · 2026Review
- Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study.Journal of advanced research · 2026Article
- Review
- Integrating Microbiomes for Regenerative Food Systems: Recent Insights, Implementations, and Emerging Trends.Food science & nutrition · 2025Review
- Sirtuins as Therapeutic Targets for Treating Cancer, Metabolic Diseases, and Neurodegenerative Diseases.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.Abdominal radiology (New York) · 2025Review
- Perinatal Shifts in Fecal-Derived Metabolites and Associations with Postpartum Depression.bioRxiv : the preprint server for biology · 2025Article
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.
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What OpenQuestion holds
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.