ReviewComputational and structural biotechnology journal2023
A guide to multi-omics data collection and integration for translational medicine.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses 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
88 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.Journal of translational medicine · 2025Pooled it
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- Reconstructing bovine disease trajectories through integrative multi-omics: molecular decision nodes, predictive biomarkers and precision intervention.Veterinary research communications · 2026Review
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Alzheimer's Disease as a Multi-Layer Network Disorder: A Systems Biology Framework Integrating Multi-Omics Mechanisms.Biomedicines · 2026Review
- Article
- Heat shock protein-mediated remodeling of the bone immune microenvironment: mechanisms and precision therapeutic strategies for osteoporosis.Journal of translational medicine · 2026Review
- Multi-omics data integration using time-to event endpoint and supervised Cox penalized regression: a comprehensive review.Clinical and experimental medicine · 2026Review
- Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.International urology and nephrology · 2026Article
- Article
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- Bridging the gap: combining treat-to-target and difficult-to-treat strategies in the management of rheumatoid arthritis.Nature reviews. Rheumatology · 2026Review
- Personalizing treatment of pancreatitis-associated chronic pain: the need for an integrated omics approach.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Review
- Multiomics for Risk Stratification in Atherosclerotic Cardiovascular Disease.Circulation. Genomic and precision medicine · 2026Review
- Human Systems Immunology in the Omics Era: Challenges, Methods, and Emerging Directions.European journal of immunology · 2026Review
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Integrative bioinformatics approaches for early detection biomarkers in ovarian cancer.Annals of medicine and surgery (2012) · 2026Review
- Current Insights and Future Directions of Multiomic and Spatial-Omic Analysis in Non-Small Cell Lung Cancer.JCO precision oncology · 2026Review
28 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emerging high-throughput technologies have led to the shift in the design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods. Currently, there is no clear consensus on the best combination of omics to include and the data integration methodologies required for their analysis. This article aims to guide the design of multi-omics studies in the field of translational medicine regarding the types of omics and the integration method to choose. We review articles that perform the integration of multiple omics measurements from patient samples. We identify five objectives in translational medicine applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, and (v) understand regulatory processes. We describe common trends in the selection of omic types combined for different objectives and diseases. To guide the choice of data integration tools, we group them into the scientific objectives they aim to address. We describe the main computational methods adopted to achieve these objectives and present examples of tools. We compare tools based on how they deal with the computational challenges of data integration and comment on how they perform against predefined objective-specific evaluation criteria. Finally, we discuss examples of tools for downstream analysis and further extraction of novel insights from multi-omics datasets.
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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.